senstick: comprehensive sensing platform with an ultra...

17
Research Article SenStick: Comprehensive Sensing Platform with an Ultra Tiny All-In-One Sensor Board for IoT Research Yugo Nakamura, Yutaka Arakawa, Takuya Kanehira, Masashi Fujiwara, and Keiichi Yasumoto Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan Correspondence should be addressed to Yugo Nakamura; [email protected] Received 13 June 2017; Revised 31 August 2017; Accepted 20 September 2017; Published 26 October 2017 Academic Editor: Alberto J. Palma Copyright © 2017 Yugo Nakamura et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. We propose a comprehensive sensing platform called SenStick, which is composed of hardware (ultra tiny all-in-one sensor board), soſtware (iOS, Android, and PC), and 3D case data. e platform aims to allow all the researchers to start IoT research, such as activity recognition and context estimation, easily and efficiently. e most important contribution is the hardware that we have designed. Various sensors oſten used for research are embedded in an ultra tiny board with the size of 50 mm () × 10 mm () × 5 mm () and weight around 3g including a battery. Concretely, the following sensors are embedded on this board: acceleration, gyro, magnetic, light, UV, temperature, humidity, and pressure. In addition, this board has BLE (Bluetooth low energy) connectivity and capability of a rechargeable battery. By using 110 mAh battery, it can run more than 15 hours. e most different point from other similar boards is that our board has a large flash memory for logging all the data without a smartphone. By using SenStick, all the users can collect various data easily and focus on IoT data analytics. In this paper, we introduce SenStick platform and some case studies. rough the user study, we confirmed the usefulness of our proposed platform. 1. Introduction e rapid growth of Internet of ings (IoT) has made it possible to sense many types of information from the real world. In the research area of human activity recognition, IoT devices equipped with various sensors have been widely used as a tool for sensing the activities. ere are three approaches of activity recognition studies in terms of sensor deployment. e first approach deploys sensors into the environment like a smart home. As typical sensors, camera [1], sound [2], and power consumption [3] have been used for monitoring dweller’s activities. In the second approach, sensors are attached to the human. e smartphone has been widely used [4, 5] as a sensing tool in this approach because it has many sensors (an acceleration sensor, gyro sensor, magnetic sensor, etc.) as well as a processor, a large data storage, and wireless network. Nowadays, instead of the smartphone, wearable devices such as a smartwatch are used [6]. JINS MEME [7] is a smart eyewear having the electrooculography (EOG) electrodes in the bridge of the glasses and the nose pads. It tries to measure the movement of eyes for recognizing the internal contexts such as drowsiness and tiredness. Also, we have proposed Waiston Belt [8] that can measure the abdominal circumference and posture. e third approach is a new paradigm where sensors are embedded in the various things surrounding us. Kurahashi et al. [9] have installed an acceleration sensor into a toilet paper holder for recognizing the person. HAPIfork [10] has embedded an acceleration sensor into a fork for monitoring eating activities. We have also added an acceleration sensor on a faucet of water or a remote control for sensing various activities of the dweller. We think that the spread of IoT makes more things have a sensor and network connectivity. In the following sections, we define the words “sensorize (verb)” and “sensorization (noun)” as the procedure to add the sensor and wireless network to the nonelectronic personal belongings. However, sensorization of tiny things is difficult and costly for the researcher. Nowadays, various rapid proto- typing platforms such as Arduino, Mbed, and Raspberry Pi have already spread by the maker movement. ey are cheap and useful, but there are some problems for sensorizing tiny Hindawi Journal of Sensors Volume 2017, Article ID 6308302, 16 pages https://doi.org/10.1155/2017/6308302

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Page 1: SenStick: Comprehensive Sensing Platform with an Ultra ...downloads.hindawi.com/journals/js/2017/6308302.pdf · ResearchArticle SenStick: Comprehensive Sensing Platform with an Ultra

Research ArticleSenStick Comprehensive Sensing Platform with an Ultra TinyAll-In-One Sensor Board for IoT Research

Yugo Nakamura Yutaka Arakawa Takuya KanehiraMasashi Fujiwara and Keiichi Yasumoto

Graduate School of Information Science Nara Institute of Science and Technology 8916-5 Takayama-cho Ikoma Nara 630-0192 Japan

Correspondence should be addressed to Yugo Nakamura nakamurayugons0isnaistjp

Received 13 June 2017 Revised 31 August 2017 Accepted 20 September 2017 Published 26 October 2017

Academic Editor Alberto J Palma

Copyright copy 2017 Yugo Nakamura et alThis is an open access article distributed under theCreativeCommonsAttribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

We propose a comprehensive sensing platform called SenStick which is composed of hardware (ultra tiny all-in-one sensor board)software (iOS Android and PC) and 3D case data The platform aims to allow all the researchers to start IoT research such asactivity recognition and context estimation easily and efficiently The most important contribution is the hardware that we havedesigned Various sensors often used for research are embedded in an ultra tiny board with the size of 50mm (119882) times 10mm (119867) times5mm (119863) and weight around 3 g including a battery Concretely the following sensors are embedded on this board accelerationgyro magnetic light UV temperature humidity and pressure In addition this board has BLE (Bluetooth low energy) connectivityand capability of a rechargeable battery By using 110mAh battery it can run more than 15 hours The most different point fromother similar boards is that our board has a large flash memory for logging all the data without a smartphone By using SenStickall the users can collect various data easily and focus on IoT data analytics In this paper we introduce SenStick platform and somecase studies Through the user study we confirmed the usefulness of our proposed platform

1 Introduction

The rapid growth of Internet of Things (IoT) has made itpossible to sense many types of information from the realworld In the research area of human activity recognition IoTdevices equipped with various sensors have been widely usedas a tool for sensing the activities There are three approachesof activity recognition studies in terms of sensor deployment

The first approach deploys sensors into the environmentlike a smart home As typical sensors camera [1] sound [2]and power consumption [3] have been used for monitoringdwellerrsquos activities

In the second approach sensors are attached to thehuman The smartphone has been widely used [4 5] as asensing tool in this approach because it has many sensors(an acceleration sensor gyro sensor magnetic sensor etc) aswell as a processor a large data storage and wireless networkNowadays instead of the smartphone wearable devices suchas a smartwatch are used [6] JINS MEME [7] is a smarteyewear having the electrooculography (EOG) electrodesin the bridge of the glasses and the nose pads It tries to

measure the movement of eyes for recognizing the internalcontexts such as drowsiness and tiredness Also we haveproposed Waiston Belt [8] that can measure the abdominalcircumference and posture

The third approach is a new paradigm where sensors areembedded in the various things surrounding us Kurahashiet al [9] have installed an acceleration sensor into a toiletpaper holder for recognizing the person HAPIfork [10] hasembedded an acceleration sensor into a fork for monitoringeating activities We have also added an acceleration sensoron a faucet of water or a remote control for sensing variousactivities of the dwellerWe think that the spread of IoTmakesmore things have a sensor and network connectivity In thefollowing sections we define thewords ldquosensorize (verb)rdquo andldquosensorization (noun)rdquo as the procedure to add the sensor andwireless network to the nonelectronic personal belongings

However sensorization of tiny things is difficult andcostly for the researcher Nowadays various rapid proto-typing platforms such as Arduino Mbed and Raspberry Pihave already spread by the maker movement They are cheapand useful but there are some problems for sensorizing tiny

HindawiJournal of SensorsVolume 2017 Article ID 6308302 16 pageshttpsdoiorg10115520176308302

2 Journal of Sensors

Hardware

SenStick

Soware

3D data

Sensorizeevery thing

+

IoTeducation

+ Create a community3D case data

+ Share 3D data

Multiplatform(iOSAndroidPC)+ Functions for research+ Functions for education

SenStick 2+ Shorter+ Separable+ Wireless sync+ Long life

Wireless charging model+ inner+ Wireless charging+ Sealed case

Research on IoT IoT education

SenStick 1+ Ultra tiny (3 g)+ Multisensors+ BLE+ Memory+ Battery 2017

SenStick 3+ Commercialized+ nRF52 (Cortex-M4F)

Open Source

2015

2016

Figure 1 SenStick platform including hardware software and community

thingsmentioned as followsThe size of the board is relativelylarge In addition the final deliverables usually become largerbecause the baseboard requires various additional functionssuch as a charging circuit clock and storage and those areconnected via the relatively large connector As a result itis actually difficult to develop ldquosmallrdquo sensorized things thatcan be used in the actual environment Another problem issoftware for controlling the deliverable from a smartphone Itis not easy to develop a sophisticated mobile application foreach deliverable respectively Since the essence of IoT is dataanalysis sensorization of everything should be simplified andsolution for collecting data smoothly is required

Based on above-mentioned background we starteddeveloping a platform called SenStick [11] for sensorizingvarious things more easily in 2014 Our proposed SenStickis a comprehensive platform including the hardware soft-ware 3D case data and community as shown in Figure 1SenStick aims to allow researchers to sensorize everythingand also educators to teach IoT-related techniques such as

data analysis The size of the first SenStick board releasedin 2015 (SenStick 1) is 75mm (119882) times 10mm (119867) times 5mm(119863) and its weight is around 3 g including a battery On thistiny board 8 sensors (acceleration gyro magnetic light UVtemperature humidity and pressure) flash memory BLEbattery and charging circuit are densely embedded

The second version of the hardware (called SenStick 2)released in 2016 [12] became shorter 50mm (119882) times 10mm(119867) times 5mm (119863) by dividing the board into two boards andconnecting them vertically By updating the firmware thebattery life of SenStick 2 reached 15 hours by 60mAh batteryAlso wireless data synchronization function and DeviceFirmware Update (DFU) over BLE have been implementedSenStick 2 has a derivative version (wireless chargingmodel)This version has a different battery type and charging circuitfor enabling wireless charging

In 2017 the third version of the hardware (called SenStick3) was sold from a company (httpsenstickcom) Almostall the configurations of SenStick 3 are the same as SenStick

Journal of Sensors 3

2 but the board is unified into one board again like SenStick1 The main change is the upgrade of BLE chip from nRF51 tonRF52 Since the processing power is increased we can addsome calculation process on the board

In the software part support application is developedfor mobile devices (iOS and Android) and desktop PC(LinuxmacOS andWindows) Each application canmonitorand record sensing data synchronously and can configurethe parameters of each sensor The Android version cansimultaneously record a ground truth video and sensing dataThe PC version allows a user to connect multiple SenStickssimultaneously and also provides a function to use Node-RED which provides a simple programming interface

In 3D case part we designed the data of basic 3D case ofSenStick It is possible to easily design the 3D case for variousthings by using our base 3D case data

The remaining part of the paper is organized as followsWe first introduce various existing products and comparethem with SenStick in Section 2 Next we explain the detailsof our proposed platform SenStick in Section 3 In Section 4we introduce some case studies utilizing SenStick and somesurvey results answered by students who actually used Sen-Stick platform Finally we conclude the paper in Section 5

2 Related Work

We summarize competitive sensors in the market in Table 1SensorTag [13] of Texas Instruments is the most competi-

tive sensor board similar to SenStick The sensors embeddedon SensorTag are almost the same as SenStick except for UVsensor However SensorTag does not have stand-alone datarecording function and a flash memory because this sensoris designed on the premise that sensing data are always storedon the smartphone connected by BLE Since the main targetof SensorTag is environmental sensing the sensing frequencyof motion sensors is limited up to 1Hz and a rechargeablebattery is adopted Therefore it is not suitable for activitysensing of IoT research In addition the board shape size andweight are completely different SenStick is much smaller andslimmer

IoT Smart Module [14] is a small sensor board equippedwith several sensors like SensorTag Support applicationsprovide functions to cooperate with IBM Bluemix so itis possible to smoothly perform tasks from sensing ofdata to cloud analysis However support applications onlywork on Android Similar to SensorTag of Ti it adoptsnonrechargeable coin battery Although it can set highersensing frequency a user must change the coin battery atshort intervals Also logically it cannot transmit all thesensing data in case of higher sensing frequency becausethe transmission capacity of BLE is low Therefore it is notsuitable for research that requires precise data

TSND121 [15] of ATR-Promotions is another competitivesensor because it was used in some academic research Ithas 9-axis motion sensors and ambient pressure sensor andadopts Classic Bluetooth 20 for transmitting data Becauseit only focuses on monitoring activities of a human and arobot the size of the sensor is relatively large but at most7 sensors can be simultaneously connected to a control PC

Since it always requires a PC for recording sensed data it isnot suitable for sensing our daily life out of the lab

WAX9 [16] of Axivity used in UK Biobank [17] was astreaming inertialmeasurement unit (IMU)which has a largeflash memory for logging The sensor combines a MEMSaccelerometer gyro and magnetometer with a Bluetoothlow energy (BLE) compatible radio Additionally WAX9 alsofeatures a barometric pressure sensor and temperature sensorHowever it does not have some environmental sensors suchas humidity light and UV sensor and always only distributesthe Windows-compatible software for data collection andvisualization It has already been discontinued

Based on the features of other competing sensors wesummarize the requirements for SenStick platform

(1) Hardware Requirements The SenStick platform aims tosense tiny activities that cannot be sensed by existing smart-phones and wearable devices Therefore essential functionsmust be embedded in a small sensor board and each sensormust collect data in high sampling rate Since BLE commu-nication dose not guarantee reliable data communicationit is necessary to be able to record in standalone withoutrecording devices such as a smartphone In addition it isrequired that the SenStick must have rechargeable batterysystem because a coin battery is not suitable for frequent use

(2) Software Requirements The SenStick platform aims tosimplify the data collection and analysis from sensorizedthings Therefore support software should be multiplatformIt should be compatible with the various operating systemsSupport software requires not only basic function such as datarecording but also additional functions such as simultaneousrecording of sensing data frommultiple SenSticks and simpleprogramming interface for data processing

(3) 3D Case Requirements The SenStick platform aims topromote the sensorization of everything For this purposeit is desirable to provide a 3D case of SenStick that can beattached to various belongings Further 3D cases createdby someone should be shared with other users through ourwebsite

3 Comprehensive Sensing Platform forIoT Research SenStick

Our proposed platform called SenStick consists of 3 com-ponents hardware software and 3D case In the followingsubsections we explain the details of these components Thisplatform allows a user to sensorize everything easily andefficiently We believe that it supports the rapid prototypingby researchers and is useful for IoT education (a videoillustrating SenStickrsquos hardware software and 3D case datais provided in Supplementary Material available online athttpsdoiorg10115520176308302)

31 Hardware Aswe explained in Section 1 we have updatedthe hardware three times from 2015 The explanation in thefollowing part is based on SenStick 2 developed in 2016The size of the sensor board is 55mm (119882) times 10mm (119867)

4 Journal of Sensors

Table1Com

paris

onof

competitives

ensors

SenS

tick

3Se

nStic

k 2

Sens

orTa

g (C

C265

0)Io

T Sm

art M

odul

eTS

ND

121

WA

X9

Size

(mm)

50(119882

)times10

(119867)times

10(119863

)50

(119882)times

10(119867

)times10

(119863)

42(119882

)times32

(119867)times

10(119863

)44

(119882)times

27(119867

)times11(119863

)37

(119882)times

46(119867

)times12

(119863)23

(119882)times

325(119867

)times76

(119863)

Weight(g)

35

28mdash

227

Stand-alon

erecording

Itimes

timestimes

ISm

artpho

neapp

II

Itimes

timesDesktop

app

I998779

timesI

IBa

ttery

life

15(hou

r)1(year)

1(year)

6(hou

r)6(hou

r)Ba

ttery

type

Rechargeablerem

ovable

Rechargeable

Coinbatte

ryCoinbatte

ryRe

chargeable

Rechargeable

Network

BLE

BLE

BLE

Bluetooth20

BLE

Datap

rocessing

Itimes

timestimes

timestimes

I2CEx

tension

Itimes

timestimes

timestimes

Sensors

Acceleratio

nI

II

II

Gyro

II

timesI

IMagnetic

II

II

IAirpressure

II

II

ITemperature

II

Itimes

IHum

idity

II

Itimes

timesLight

II

Itimes

timesUV

Itimes

Itimes

timesMi c

timesI

timestimes

times

Journal of Sensors 5

Flash memory(back side)

50GG

60GB

Temperature amphumidity(SHT-20)

UV(VEML6070)

Pressure(LPS25HB)

Light(BH1780GLI)

Charger(BQ24090)

Micro USB

Li-Po battery

USBUART(CP2104)

9-axis(MPU-9250)

BLE(nRF51822)

Figure 2 Hardware of SenStick 2 (8 sensors BLE flash memory etc)

Table 2 Sensors embedded in SenStick 23

Type of sensor Model number Power consumption9-Axis MPU-9250 280 120583Asim37mAPressure LPS25HBTR 4 120583Asim25 120583ATemperaturehumidity SHT20 270 120583Asim330 120583AIlluminance BH1780GLI-E2 120120583Asim200 120583AUltraviolet (UV) VEML6070 100 120583Asim250 120583A

times 5mm (119863) and its weight is around 3 (g) including thebattery On this tiny board eight sensors (acceleration gyromagnetic light UV temperature humidity and pressure)flash memory (32Mbytes) BLE battery and charging circuitare densely embedded as shown in Figure 2 The modelnumber and power consumption of the sensors embedded inSenStick are listed in Table 2 All the sensors are popular chipsused worldwide and their performances are ensured by eachmanufacturer

The novelty of our hardware is the design of the sensorboard composed of the elaborated combination to satisfythe requirements presented in Section 2 This contributionbreaks down into the following aspects

(1) Combination of BLE and Flash Memory Almost allthe BLE-based devices equipped no large flash memoryto record sensing data because these devices are designedon the premise that sensing data are always recorded onsmartphones connected via BLE However the transmissionspeed of BLE is not enough for sending all the sensing dataof multiple sensors with the high sampling frequency As aresult some data will be dropped in the transmission Sincethe design of existing BLE-based devices is not appropriatefor research purpose we designed a new BLE-based sensingboard with a large flash memory which allows users torecord all the sensing data precisely Also it means that it canwork without holding a smartphone This ability is useful forsensing themotion of animals that cannot have a smartphone

(2) Selection of Powerful and Energy-Saving SoC Weselected Nordicrsquos nRF52832 SoC as SenStickrsquos microcon-troller Because the nRF52832 SoC is built around a 32-bit ARM Cortex-M4F CPU with 512 kB + 64 kB RAM itis extremely powerful Moreover the embedded 24GHztransceiver supports Bluetooth low energy and has extremelylow power consumption and it is the worldrsquos smallest classmodule Each sensor is connected via two I2C buses inde-pendently processing high-speed sensor (acceleration gyroandmagnetic) and low-speed sensor (light UV temperaturehumidity and pressure) taking into account the differencein bus occupancy time This makes it possible to realize highsampling with the motion sensors

(3) Application-Friendly Battery Type and Shape Since Sen-Stick is designed to bemounted on objects such as chopstickstoothbrushes and glasses it is desirable that the shape is thinand compact However sensors such as SensorTag equippedwith readily available coin batteries are inappropriate inshape because the footprint is square Also in the researchnonrechargeable coin batteries are costly and troublesomeFor those reasons we selected a thin and compact lithiumpolymer battery on SenStick This type of battery was harderto obtain than expected Therefore the procurement ofbattery has become the bottleneck of development

SenStickrsquos firmware is newly designed and developed byconsidering aforementioned requirements Generic Attribute(GATT) profile of SenStick provided by firmware is shownin Figure 3 The GATT server in SenStick provides threeservices (SenStick Control Service Metadata Read Serviceand Sensor Service) The SenStick Control Service providesoperation instructions such as starting and stopping sensingand logging The metadata reading service provides thefunction of reading metainformation of log data The SensorService provides functions to specify the sensing and loggingoperation of each sensor (0 acceleration 1 gyro 2 magnetic3 light 4 UV 5 temperaturehumidity 6 pressure) Also itprovides real-time sensing data reading function and log datareading function

6 Journal of Sensors

(32-)

Peripheral

GATT server

SenStick Control Service

Metadata Read Service

Read-write

Data notication

Flashmemory

(0 acceleration 1 gyro 2 magnetic 3 light

4 UV 5 temperaturehumidity 6 pressure)

GATT client

Central

~Sensor Service (0 6)

Figure 3 Generic Attribute (GATT) profile of SenStick

Table 3 BLE communication specification

Type of content SpecAdvertising packet Complete UUID flagScan response Local name (SenStick)Advertising packet interval 100ms (30 s)rarr1000ms (150 s)rarrsleepConnection interval max 80msmin 20msSupervision timeout 4000msSlave latency 0

Basically all the sensing data will be notified to a smart-phone in real time up to a transmission capacity of BLE Ifthe amount of sensing data exceeds the transmission capacitya part of sensing data will be dropped Therefore the real-time sensing data reading function is used formonitoring thestatus or for an interactive application that does not requireprecise data For precise data acquisition the log data readingfunction which guarantees the data reading order is usedThe communication specifications of BLE are described inTable 3

As shown in Table 4 the fixed capacities of a flashmemory are allocated to each sensorThe number of samplesindicates the total number of samples that can be recorded bythe allocated capacities

SenStick stops logging when the number of samples ofone sensor exceeds the storage capacity allocated for it Themaximum recording time of acceleration gyroscope andmagnetic sensor is 4 hours 40 minutes at sampling intervalof 10 milliseconds (sampling frequency 100Hz) and 14 hours10 minutes at sampling period of 30 milliseconds (samplingfrequency 33Hz) The maximum recording time of othersensors is 9 hours 26 minutes at sampling period of 200milliseconds (sampling frequency 5Hz)

32 Software We provide mobile applications for iOS andAndroid and a library for nodejs platform Common func-tions of them are operating function parameter settingfunction and real-time data view function An additional

Table 4 Allocated storage capacity per sensor

Amount of allocatedstorage capacity Samples

Acceleration 102Mbytes 17M samplesGyro 102Mbytes 17M samplesMagnetic 102Mbytes 17M samplesIlluminance 340KB 170K samplesUV (UV) 340KB 170K samplesHumidity 170KB 170K samplesAir temperature 170KB 170K samples

function of iOS application is a firmware update functionThose of Android application and nodejs applications areground truth video recording function and an ability to con-nect multiple SenSticks respectively Since all these softwareapplications are shared on GitHub as an own source softwareall the users can create their application based on their ownpurposes

Communication Protocol Here we explain the communica-tion protocol between SenStick and softwareThe explanationis based on ldquonode-SenStickrdquo which is our developed libraryfor nodejs Figure 4 shows a protocol sequence betweenSenStick and the central terminal (node-SenStick on Mac)

(I) Discovery First of all the application needs to find thesurrounding SenStick by using SenStickdiscovery() methodin our library When SenStickdiscovery() method finds aSenStick a callback function with an instance of SenStickobject containing the SenStickrsquos UUID and LocalName asarguments is called

(II) Connect If the target SenStick is found by the discoveryprocess the application is going to establish a connec-tion with it by calling SenStickprototypeconnectAndSetUp()method After establishing the connection this methodacquires the information necessary for operation from thetarget SenStick

Journal of Sensors 7

Central

Node-SenStick SenStick

SenStickdiscovery()

SenStickprototypeconnectAndSetUp()

Establish connection

SenStickprototypewriteSensorMeasurementCong()

Reect setting

SenStickprototypestartSensingAndLogging()

SenStickprototypenotifySensor()Start sensing

Select sensor to notify

Sensing data

SenStickprototypestopSensingAndLogging()

Stop sensing

Enable log notication

Select log data to notify

SenStickprototypewriteSensorLogReadoutTargetID()

Logging data

SenStickprototypedisconnect()

Disconnect

SenStickprototypenotifySensorLogData()

Periperal

(I) Discovery

(II) Connect

(III) Setting

(V) Obtaining log

(VI) Disconnect

(IV) Start sensingamp

RT receiving

Figure 4 Communication sequence between SenStick and node-SenStick

(III) Setting After establishing the connection the appli-cation sets three parameters (operation mode samplinginterval and measurement range) for each sensor by usingSenStickprototypewriteSensorMeasurementConfig()methodThe operation mode is a parameter that sets valida-tioninvalidation of sensing and logging of each sensor Thesampling interval and measurement range are parameters ofeach sensorwhichwill be changed by the sensing purpose andapplications

(IV) Start Sensing and Real-Time Data Receiving When Sen-StickprototypestartSensingAndLogging() method is calledSenStick starts sensing and logging Only valid sensorsrsquodata are transmitted via BLE and also recorded into aflash memory ID of each log is automatically assignedDuring sensing SenStick notifies sensing data in realtime To receive this notification the application callsSenStickprototypenotifySensor() method (Sensor representseach sensor name) and activates the notification for

8 Journal of Sensors

(i) Frequency(ii) Range

Device select Main view Settings Firmware update

Real-time view Log list in SenStick Download from SenStick

Selected sensorSensor name

Sensing frequency

Range of y-axis

Figure 5 iOS application for SenStick

the sensor When a notification of sensing data isreceived sensorChange event will be issued If Sen-StickprototypestopSensingAndLogging() method is calledSenStick stops sensing and logging

(V) Log Data Reading To read the log data recordedin the flash memory to the application the applicationcalls SenStickprototypenotifySensorLogData()method (Sen-sor represents each sensor name) and enables log datanotification If the application specifies a valid log ID bycalling SenStickprototypewriteSensorLogReadoutTargetID()method SenStick starts informing the sensing data recordedin the log sequentially To receive notification of log datasensorLogDataReceived event will be issued By receiving thisnotification by the application it is possible to acquire the logdata recorded in the memory

(VI) Disconnect After finishing the data acquisition Sen-Stickprototypedisconnect() method will be called to releasethe connection of SenStick And SenStick will begin advertis-ing again

User InterfaceWe explain the user interface of the applicationbased on iOS version Figure 5 shows a sophisticated mobileapplication for SenStick As shown in Figure 5 the userselects target SenStick from devices listThen the applicationshows the main view In the main view the user can selectsensors to be acquired For immediate data monitoring allthe received data via BLE can be shown in the graph view inreal time The application can control all the settings of eachsensor such as sample frequency and sensing range And alsoit has firmware update function Sensing data stored in theflash memory of SenStick is viewed from log list and it can be

downloaded to the smartphone in CSV formatThis functionprevents data loss caused by BLE communication

33 3D Case Data In order to make sensorized things anattachment case for mounting the SenStick is required Wedesigned the basic 3D case of SenStick By extending thisbasic case users can design derivative cases for chopsticksglasses and so on Actually our laboratory has succeeded indesigning cases for several things as shown in Figure 6 Allof these cases data are shared on our community site Eachcase is designed in a few minutes thanks to the basic caseFor example SenStick case for glasses is made by combiningbasic case of SenStick and open 3D data of glasses distributedby i Design Studio In addition due to the popularizationof 3D printers in the world and enhancement of tools andreferences for 3D modeling all the users can print out theirdesired SenStick cases by themselves and attach our sensor toeverything

4 Case Studies of SenStick

SenStick an ultra small sensor can be embedded into varioustiny things in our daily life Our first target is our dailybelongings such as glasses chopsticks and pens becausesensorization of daily belongings is good to intuitively under-standwhat can be sensed andwhich sensor is usefuleffectiveSenStick can upgrade normal glasses to intelligent oneswhichcan monitor a head movement posture and the amount ofsunlight received If SenStick is embedded into chopsticksand pens we can observe eating activities and handwritingactivities respectively In the future we will embed it into astick for elderlymonitoring sports gearsmusical instrument

Journal of Sensors 9

Chopsticks Tooth brush Glasses Umbrella Name card Cookware

A user can design original case data in a few minutes

Basic 3D case of SenStick

Figure 6 3D case data of SenStick

Sensorized

Chopsticks

Figure 7 Eating activity recognition by sensorized chopsticks

kitchen utensils animals stuffed toy a remote control andany kind of things that can be sensing targets In this sectionwe introduce some case studies results utilizing SenStick

41 Eating Behavior Recognition by Sensorized Chopsticks Inmany cases East Asian people eat meals with chopsticksBy analyzing the data obtained from chopsticks there is apossibility that we can estimate the eating habits includinganswers for questions like ldquohow did you eatrdquo and ldquowhatdid you eatrdquo and so forth These data are very useful formaking healthcare applications and leading a healthy lifeThis is one reason why we develop the sensorized chopsticksusing SenStick shown in Figure 7

Research goals of the eating behavior recognition by sen-sorized chopsticks are as follows G1 (behavior) recognizingthe timing and speed and detecting ldquocroprdquo and ldquointakerdquoaction G2 (food type) classifying food content like ricenoodles and so forth from the eating behavior and G3(person) identifying an individual from using chopsticks Inthis paper we show the research results on G1 and G2

We conducted basic experiments to recognize the eatingbehavior (G1 and G2) We used acceleration and gyroscopedata measured by SenStick where the sensing rate is 10HzThe purpose of experiment 1 is to understand the basic

characteristics of sensing data from sensorized chopstickwhen a participant ate chips The purpose of experiment 2 isto identify the food type from the difference in movement ofchopsticks We assumed 4 classes (C1 rice and dish C2 misosoup C3 noodles and C4 shrimp) as food types

Figure 8 shows time series of sensing data for a set ofeating actions in experiment 1 The eating actions are cropaction and intake action In crop action 6-axis motionsensors detect movement (open and close) of the chopsticksIn intake action sensors detect movement of the arm fromthe dish to the mouth Each graph in Figure 8 shows certaincorrelation with both actions

Figure 9 shows a typical signal for each food in exper-iment 2 In this experiment we calculate the recognitionaccuracy of food type classification by using Random Forestclassier ofWeka Tables 5 and 6 show the classification resultsThe average recognition accuracy was 896 If we did nothave a SenStick it would be hard to develop the sensorizedchopsticks that can be used in the actual environment

42 Daily Activity Monitoring by a Sensorized Belt Recentlycorpulent people are rapidly increasing and about 700 mil-lion people belong to the obesity group [18] Nowadays manyoffice workers use computers heavily and their sitting time

10 Journal of Sensors

Step 1 crop action Step 2 intake action

Lower

Upper

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

X

Y

Z

Synthetic

X

Y

Z

Synthetic

X

Y

Z

Synthetic

X

Y

Z

Synthetic

minus250

minus150

minus50

50

150

250

Ang

ular

velo

city

(rad

s)

minus20

minus10

01020

Acce

lera

tion

(ms

2)

minus250

minus150

minus50

50

150

250

Ang

ular

velo

city

(rad

s)

minus20

minus10

01020

Acce

lera

tion

(ms

2)

Open1 Close2 Raise3 Eat4 Drop5

1 2 3 4 5

Figure 8 Time series of sensing data in eating activity

in office increases Keeping incorrect postures during longworking time increases the risk of obesity of office workersTherefore it is necessary tomonitor the daily activity of officeworkers for correcting the bad habits

Motivated by this we have developed a sensorized beltcalled Waiston Belt [8] which aims to monitor a userrsquosdaily activities for maintaining health As products of belt-type wearable devices Welt [19] and Belty [20] are on saleThese provide the function of waist measurement activity

tracking and step counting However API for obtainingsensing data is not provided and it is not suitable for researchpurposes Generally it is hard for ordinary researchers toembed sensors battery BLE and vibrator into a small buckle-size case that can be worn in everyday life Nam et al [21]have proposed a belt-attached device for monitoring postureHowever the device has not reached a sufficient level to beused on a daily basis because of exposed wiring and uncoolappearance However by extending SenStick we succeeded

Journal of Sensors 11

C1

C2 C3

C4

Typical signal of each food

C1 rice amp dish C2 miso soup C3 noodles C4 shrimp

400

200

0

minus200

minus400

Figure 9 Eating activity recognition by sensorized chopsticks

SenStick

Waiston Belt

Figure 10 Waiston Belt based on SenStick

Table 5 Experimental result (food type)

Food type Number ofpieces of data Precision Recall 119865-Measure

C1 rice and dish 3011 0894 0945 0919C2 miso soup 2187 0907 0841 0873C3 noodles 2761 088 0896 0888C4 shrimp 2091 0907 088 0893

0896 0896 0895

to develop a stylish prototype of Waiston Belt shown inFigure 10 quickly Since the battery life of SenStick is 15 hourswith a small battery (60mAh) we can sense continuously inour daily lives As shown in Figure 11 it is possible to estimatevarious usersrsquo contexts (How much did you walk Are youusing the stairs Is posture good at work) by analyzing timeseries of sensing data obtained from the sensorized belt

An important point in this use case is that usersrsquo contextscan be estimated by combining multiple sensing data For

Table 6 Confusion matrix of food type recognition

PredictedC1 C2 C3 C4

ActualC1 rice and dish 094 004 001 000C2 miso soup 012 084 004 000C3 noodles 002 002 090 006C4 shrimp 001 001 011 088

example climbing stairs is estimated from the changes ofacceleration and atmospheric pressure The change of thehumidity data will be helpful to detect that a user enteredan air-conditioned room A SenStick makes it possible toeasily carry out research on behavior recognition with acombination of such multiple sensors

We introduce basic experiments about the posture recog-nition during deskwork by using this beltThe purpose of thisexperiment is to identify the posture type from the attitude

12 Journal of Sensors

Enter the lab

HumidityTemp

Humidity dropping

Pressure dropping

Working

Walking WalkingClimbing stairs Sitting (good posture) Sitting (bad posture)

AccsXAccsYAccsZ

60

40

20

1003

1002

1001

1000

999

25

2

15

1

05

0

minus15

minus1

minus05

Air pressure

Figure 11 Time series of sensing data in daily activity

C1 good posture C2 bad posture 1

C3 bad posture 2 C4 bad posture 3

Figure 12 Time series of sensing data in posture monitoring

angle of the belt calculated from 3 axial acceleration sensorsWe set the sensing rate as 10Hz and collected 6000 samples(10min) for each posture We assumed 4 classes (C1 goodposture C2 bad posture 1 C3 bad posture 2 and C4 badposture 3) as shown in Figure 12

In this experiment we calculated the recognition accu-racy of posture type classification by using Random Forestclassifier of Weka Tables 7 and 8 show the classificationresults The average recognition accuracy was 997 Inthe future based on the result of posture estimation we

Journal of Sensors 13

Sensorized glasses

Figure 13 Sensorized glasses

Table 7 Experimental result (posture type)

Posture type Number ofpieces of data Precision Recall 119865-Measure

C1 good posture 6000 1000 1000 1000C2 bad posture 1 6000 0994 0994 0994C3 bad posture 2 6000 0994 0994 0994C4 bad posture 3 6000 0999 0999 0999

0997 0997 0997

will develop a software application that suggests correctinghisher bad posture to a right posture during desk work

43 Indoor Localization by Sensorized Glasses We have pro-posed an indoor localization system using illuminance-basedtrilateration method [22] In this system we assume that theperson wears glasses having an illuminance sensor as shownin Figure 13 and controllable light sources are deployed in thetarget environment Our system controls lights andmeasuresthe variation of illuminance at the glasses As shown inFigure 14 the distances (1198891ndash1198893) from each light (1198971ndash1198973) to theuser (119906) are estimated from the distance-illumination modelconstructed in advance and userrsquos position is estimated basedon trilateration method using estimated distances

As a result of an experiment where three controllablelight sources were deployed in a room we confirmed thatour system could estimate the position within 05m erroron average By using SenStick we could easily develop thesensorized glasses equipped with illuminance sensor

44 Sensorized Airsoft Gun We have developed the sen-sorized airsoft gun for activity recognition in airsoft gameas shown in Figure 15 Airsoft game is a sport in whichparticipants eliminate opponents by hitting each other usingreplica weapons called airsoft guns The graph of a typicalsignal from the sensorized airsoft gun is shown in Figure 16A user can count the number of shots of a ball by attachingSenStick to his airsoft gun

45 Another Example Studentrsquos Unique Achievements andSurvey Results SenStick is also used in a lecture of IoT

Table 8 Confusion matrix of posture type recognition

PredictedC1 C2 C3 C4

ActualC1 good posture 1000 0000 0000 0000C2 bad posture 1 0000 0994 0006 0001C3 bad posture 2 0000 0006 0994 0000C4 bad posture 3 0000 0001 0000 0999

prototyping Students realize their unique IoT devices easilyby using SenSticks Here we introduce some of the developedIoT devices and survey results about the usefulness ofSenStick answered by students

Figure 17 shows a sensorized pencil with two SenSticksThe sensorized pencil is developed to recognize the charac-ters written by this pencil Even though two SenSticks areembedded to detect precise movement of pencil the size ofthe pencil is reasonable

Figure 18 shows a sensorized name tag anddigital signageSince we changed the battery from 60mAh to 900mAh thetag keeps running more than one week It aims to recordemployeesrsquo or studentsrsquo daily activities to support their healthThe digital signage informs the user who wears a sensorizedname tag about the userrsquos activity data when the user standsin front of it

Figure 19 shows a sensorized spoon used in the digitalfish catching game for children In this game the sensorizedspoon detects the scooping movement of the hand Thuschildren can catch the digital fish displayed by the projectionmapping by using sensorized spoonThis systemwas actuallyused at the universityrsquos open campus event and around100 children enjoyed this game and showed interest in IoTtechnology

The survey results obtained from the ten graduate stu-dents (ninemales and one female) who developed prototypesas mentioned above are shown in Table 9 A questionnaireis a five-grade form (5 strongly agree 4 agree a little 3neither agree nor disagree 2 disagree a little and 1 stronglydisagree) From the results of questions 1 and 2 manyparticipants think that SenStick platform is useful for IoT

14 Journal of Sensors

Position

Distance illumination model

Using trilateration method

a

Light 3

Light 2

Light 1

L = minus421d + 1496

L = minus063d + 501

05 1 15 2 25 3 35 4 450Distance between the illuminance sensor and the lighting device (m)

02468

1012141618

Illum

inan

ce v

alue

(lux

)

Distanceu

d1

d2

d3

l1 p1 x1

l3 p3 x3

l2 p2 x2

Figure 14 Indoor localization by sensorized glasses

Sensorized airsoft gun

Figure 15 Sensorized airsoft gun

research and IoT education Also the results of question 3show that many students are interested in the SenStick andwant to use it again From the results of questions 4 and 5we can see that usability and specification of SenStick arerelatively high There were opinions about the usefulness ofSenStick platform such as ldquoI could quickly try out ideas of IoTprototypingrdquo ldquoIt was easy to collect sensing datardquo and ldquoSinceSenStick is small it was easily attached tomost of the thingsrdquoOn the other hand there were opinions about improvements

such as ldquoI want to use higher sensing frequency (50ndash100Hz)rdquoldquoI want to process a sensing data on SenStickrdquo and ldquoI want tomodify the firmware itself depending on the application ofthe IoT devicerdquo

5 Conclusion

In this paper we have introduced a smart and comprehensivesensing platform called SenStick which is composed of

Journal of Sensors 15

Typical signal of airso gun

Hold a gun Ready

Shoot

1 2 3 4 5 6 70

00

30

60

91

21

51

82

12

42

73

03

33

63

94

24

54

85

15

45

76

06

36

66

97

27

57

88

18

48

79

09

39

69

910

210

510

811

111

411

712

012

312

612

913

2

minus600

minus400

minus200

0

200

400

600

800

acc_xacc_yacc_z

gyro_xgyro_y

acc_synthesis gyro_zgyro_synthesis

Figure 16 Time series of sensing data in shooting an airsoft gun

Sensorized pencil

Figure 17 Sensorized pencil

Sensorized name tagVisualization of sensor data

Figure 18 Sensorized name tag

Sensorized spoon Digital fishing game

Figure 19 Sensorized spoon

Table 9 Questionnaire results about SenStick

Question contents ScoreQ1 Do you think that SenStick will be useful for IoTresearch 49

Q2 Do you think that SenStick will be useful for IoTeducation 47

Q3 Do you want to use SenStick again 49Q4 Do you think that SenStick was easy to use 41Q5 Are you satisfied with specification of SenStick 41

hardware software and 3D case data The main purpose ofour platform is to simplify the sensorization of our personalbelongings

The hardware of SenStick is maybe one of the worldrsquossmallest sensor boards which includes 8 kinds of sensorsand BLE In addition to the hardware we also developedsupport software for managing monitoring recording andviewing the hardware By providing 3D case data all theresearchers can measure every tiny activity in daily life easilyand efficiently For an educational use it is also useful forunderstanding sensors and prototyping the desired systemincluding tiny censored things

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

This work is supported in part by JST PRESTO and JSPSKAKENHI (Grant no 17J10021) and ldquoResearch and Devel-opment of Innovative Network Technologies to Create theFuturerdquo the Commissioned Research of National Instituteof Information and Communications Technology (NICT)Japan

16 Journal of Sensors

References

[1] O Brdiczka M Langet J Maisonnasse and J L CrowleyldquoDetecting human behavior models from multimodal observa-tion in a smart homerdquo IEEE Transactions on Automation Scienceand Engineering vol 6 no 4 pp 588ndash597 2009

[2] J Chen A Kam J Zhang N Liu and L Shue ldquoBathroomactivity monitoring based on soundrdquo in Pervasive ComputingThird International Conference PERVASIVE 2005 MunichGermanyMay 8-13 2005 Proceedings vol 3468 ofLectureNotesin Computer Science pp 47ndash61 Springer Berlin Germany2002

[3] G Bauer K Stockinger and P Lukowicz ldquoRecognizing theUse-Mode of Kitchen Appliances from Their Current Consump-tionrdquo in Smart Sensing and Context vol 5741 of Lecture Notesin Computer Science pp 163ndash176 Springer Berlin HeidelbergBerlin Heidelberg 2009

[4] K Ouchi and M Doi ldquoIndoor-outdoor activity recognition bya smartphonerdquo in Proceedings of the the 2012 ACM Conferencep 537 Pittsburgh Pennsylvania September 2012

[5] A Anjum and M U Ilyas ldquoActivity recognition using smart-phone sensorsrdquo in Proceedings of the IEEE 10th ConsumerCommunications and Networking Conference (CCNC rsquo13) pp914ndash919 IEEE January 2013

[6] O D Lara and M A Labrador ldquoA survey on human activityrecognition using wearable sensorsrdquo IEEE CommunicationsSurveys amp Tutorials vol 15 no 3 pp 1192ndash1209 2013

[7] ldquoJins memerdquo httpsjins-memecom[8] Y Matsuda T Ishioka T Hasegawa et al ldquoWaistonBelt A belt

for monitoring your real abdominal circumference foreverrdquo inProceedings of the ACM International Joint Conference on Perva-sive and Ubiquitous Computing and the 2015 ACM InternationalSymposium on Wearable Computers UbiComp and ISWC 2015pp 329ndash332 September 2015

[9] M Kurahashi K Murao T Terada and M Tsukamoto ldquoPer-sonal identification system based on rotation of toilet paperrollsrdquo in Proceedings of the IEEE International Conference onPervasive Computing and CommunicationsWorkshops (PerComWorkshops rsquo17) pp 521ndash526 IEEE Kona Hawaii USA March2017

[10] ldquoHapiforkrdquo httpswwwhapicom[11] Y Arakawa ldquoSenStick Sensorize every thingsrdquo in Proceedings of

the ACM International Joint Conference on Pervasive and Ubiq-uitous Computing and the 2015 ACM International SymposiumonWearable Computers UbiComp and ISWC 2015 pp 349ndash352September 2015

[12] Y Nakamura T Kanehira Y Arakawa and K YasumotoldquoSenStick 2 Ultra tiny all-in-one sensor with wireless chargingrdquoinProceedings of the 2016ACM International Joint Conference onPervasive and Ubiquitous Computing UbiComp 2016 pp 337ndash340 September 2016

[13] ldquoSensortag cc2541 (texas instruments)rdquo httpwwwticomtoolcc2541dk-sensor

[14] ldquoIot smart modulerdquo httpwwwalpscomjiotsmart[15] ldquoTsnd121 (atr-promotions)rdquo httpwwwatr-pcomproducts

TSND121html[16] ldquoWax9 (axivity)rdquo httpaxivitycomproductwax9[17] Uk biobank ldquoUk biobankrdquo httpwwwukbiobankacuk[18] J C Seidell and J Halberstadt ldquoThe global burden of obesity

and the challenges of preventionrdquo Annals of Nutrition andMetabolism vol 66 supplement 2 pp 7ndash12 2015

[19] ldquoWeltThe smart belt for fashion and healthrdquo httpswwwwelt-corpcom

[20] ldquoBelty power The belt your really deserverdquo httpswwwbeltyco

[21] H Nam J-H Kim and J-I Kim ldquoSmart Belt A wearabledevice for managing abdominal obesityrdquo in Proceedings of theInternational Conference on Big Data and Smart ComputingBigComp 2016 pp 430ndash434 January 2016

[22] K Moriya M Fujimoto Y Arakawa and K Yasumoto ldquoIndoorlocalization based on distance-illuminance model and activecontrol of lighting devicesrdquo in Proceedings of the 2016 Interna-tional Conference on Indoor Positioning and Indoor NavigationIPIN 2016 pp 1ndash6 October 2016

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal of

Volume 201

Submit your manuscripts athttpswwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

Page 2: SenStick: Comprehensive Sensing Platform with an Ultra ...downloads.hindawi.com/journals/js/2017/6308302.pdf · ResearchArticle SenStick: Comprehensive Sensing Platform with an Ultra

2 Journal of Sensors

Hardware

SenStick

Soware

3D data

Sensorizeevery thing

+

IoTeducation

+ Create a community3D case data

+ Share 3D data

Multiplatform(iOSAndroidPC)+ Functions for research+ Functions for education

SenStick 2+ Shorter+ Separable+ Wireless sync+ Long life

Wireless charging model+ inner+ Wireless charging+ Sealed case

Research on IoT IoT education

SenStick 1+ Ultra tiny (3 g)+ Multisensors+ BLE+ Memory+ Battery 2017

SenStick 3+ Commercialized+ nRF52 (Cortex-M4F)

Open Source

2015

2016

Figure 1 SenStick platform including hardware software and community

thingsmentioned as followsThe size of the board is relativelylarge In addition the final deliverables usually become largerbecause the baseboard requires various additional functionssuch as a charging circuit clock and storage and those areconnected via the relatively large connector As a result itis actually difficult to develop ldquosmallrdquo sensorized things thatcan be used in the actual environment Another problem issoftware for controlling the deliverable from a smartphone Itis not easy to develop a sophisticated mobile application foreach deliverable respectively Since the essence of IoT is dataanalysis sensorization of everything should be simplified andsolution for collecting data smoothly is required

Based on above-mentioned background we starteddeveloping a platform called SenStick [11] for sensorizingvarious things more easily in 2014 Our proposed SenStickis a comprehensive platform including the hardware soft-ware 3D case data and community as shown in Figure 1SenStick aims to allow researchers to sensorize everythingand also educators to teach IoT-related techniques such as

data analysis The size of the first SenStick board releasedin 2015 (SenStick 1) is 75mm (119882) times 10mm (119867) times 5mm(119863) and its weight is around 3 g including a battery On thistiny board 8 sensors (acceleration gyro magnetic light UVtemperature humidity and pressure) flash memory BLEbattery and charging circuit are densely embedded

The second version of the hardware (called SenStick 2)released in 2016 [12] became shorter 50mm (119882) times 10mm(119867) times 5mm (119863) by dividing the board into two boards andconnecting them vertically By updating the firmware thebattery life of SenStick 2 reached 15 hours by 60mAh batteryAlso wireless data synchronization function and DeviceFirmware Update (DFU) over BLE have been implementedSenStick 2 has a derivative version (wireless chargingmodel)This version has a different battery type and charging circuitfor enabling wireless charging

In 2017 the third version of the hardware (called SenStick3) was sold from a company (httpsenstickcom) Almostall the configurations of SenStick 3 are the same as SenStick

Journal of Sensors 3

2 but the board is unified into one board again like SenStick1 The main change is the upgrade of BLE chip from nRF51 tonRF52 Since the processing power is increased we can addsome calculation process on the board

In the software part support application is developedfor mobile devices (iOS and Android) and desktop PC(LinuxmacOS andWindows) Each application canmonitorand record sensing data synchronously and can configurethe parameters of each sensor The Android version cansimultaneously record a ground truth video and sensing dataThe PC version allows a user to connect multiple SenStickssimultaneously and also provides a function to use Node-RED which provides a simple programming interface

In 3D case part we designed the data of basic 3D case ofSenStick It is possible to easily design the 3D case for variousthings by using our base 3D case data

The remaining part of the paper is organized as followsWe first introduce various existing products and comparethem with SenStick in Section 2 Next we explain the detailsof our proposed platform SenStick in Section 3 In Section 4we introduce some case studies utilizing SenStick and somesurvey results answered by students who actually used Sen-Stick platform Finally we conclude the paper in Section 5

2 Related Work

We summarize competitive sensors in the market in Table 1SensorTag [13] of Texas Instruments is the most competi-

tive sensor board similar to SenStick The sensors embeddedon SensorTag are almost the same as SenStick except for UVsensor However SensorTag does not have stand-alone datarecording function and a flash memory because this sensoris designed on the premise that sensing data are always storedon the smartphone connected by BLE Since the main targetof SensorTag is environmental sensing the sensing frequencyof motion sensors is limited up to 1Hz and a rechargeablebattery is adopted Therefore it is not suitable for activitysensing of IoT research In addition the board shape size andweight are completely different SenStick is much smaller andslimmer

IoT Smart Module [14] is a small sensor board equippedwith several sensors like SensorTag Support applicationsprovide functions to cooperate with IBM Bluemix so itis possible to smoothly perform tasks from sensing ofdata to cloud analysis However support applications onlywork on Android Similar to SensorTag of Ti it adoptsnonrechargeable coin battery Although it can set highersensing frequency a user must change the coin battery atshort intervals Also logically it cannot transmit all thesensing data in case of higher sensing frequency becausethe transmission capacity of BLE is low Therefore it is notsuitable for research that requires precise data

TSND121 [15] of ATR-Promotions is another competitivesensor because it was used in some academic research Ithas 9-axis motion sensors and ambient pressure sensor andadopts Classic Bluetooth 20 for transmitting data Becauseit only focuses on monitoring activities of a human and arobot the size of the sensor is relatively large but at most7 sensors can be simultaneously connected to a control PC

Since it always requires a PC for recording sensed data it isnot suitable for sensing our daily life out of the lab

WAX9 [16] of Axivity used in UK Biobank [17] was astreaming inertialmeasurement unit (IMU)which has a largeflash memory for logging The sensor combines a MEMSaccelerometer gyro and magnetometer with a Bluetoothlow energy (BLE) compatible radio Additionally WAX9 alsofeatures a barometric pressure sensor and temperature sensorHowever it does not have some environmental sensors suchas humidity light and UV sensor and always only distributesthe Windows-compatible software for data collection andvisualization It has already been discontinued

Based on the features of other competing sensors wesummarize the requirements for SenStick platform

(1) Hardware Requirements The SenStick platform aims tosense tiny activities that cannot be sensed by existing smart-phones and wearable devices Therefore essential functionsmust be embedded in a small sensor board and each sensormust collect data in high sampling rate Since BLE commu-nication dose not guarantee reliable data communicationit is necessary to be able to record in standalone withoutrecording devices such as a smartphone In addition it isrequired that the SenStick must have rechargeable batterysystem because a coin battery is not suitable for frequent use

(2) Software Requirements The SenStick platform aims tosimplify the data collection and analysis from sensorizedthings Therefore support software should be multiplatformIt should be compatible with the various operating systemsSupport software requires not only basic function such as datarecording but also additional functions such as simultaneousrecording of sensing data frommultiple SenSticks and simpleprogramming interface for data processing

(3) 3D Case Requirements The SenStick platform aims topromote the sensorization of everything For this purposeit is desirable to provide a 3D case of SenStick that can beattached to various belongings Further 3D cases createdby someone should be shared with other users through ourwebsite

3 Comprehensive Sensing Platform forIoT Research SenStick

Our proposed platform called SenStick consists of 3 com-ponents hardware software and 3D case In the followingsubsections we explain the details of these components Thisplatform allows a user to sensorize everything easily andefficiently We believe that it supports the rapid prototypingby researchers and is useful for IoT education (a videoillustrating SenStickrsquos hardware software and 3D case datais provided in Supplementary Material available online athttpsdoiorg10115520176308302)

31 Hardware Aswe explained in Section 1 we have updatedthe hardware three times from 2015 The explanation in thefollowing part is based on SenStick 2 developed in 2016The size of the sensor board is 55mm (119882) times 10mm (119867)

4 Journal of Sensors

Table1Com

paris

onof

competitives

ensors

SenS

tick

3Se

nStic

k 2

Sens

orTa

g (C

C265

0)Io

T Sm

art M

odul

eTS

ND

121

WA

X9

Size

(mm)

50(119882

)times10

(119867)times

10(119863

)50

(119882)times

10(119867

)times10

(119863)

42(119882

)times32

(119867)times

10(119863

)44

(119882)times

27(119867

)times11(119863

)37

(119882)times

46(119867

)times12

(119863)23

(119882)times

325(119867

)times76

(119863)

Weight(g)

35

28mdash

227

Stand-alon

erecording

Itimes

timestimes

ISm

artpho

neapp

II

Itimes

timesDesktop

app

I998779

timesI

IBa

ttery

life

15(hou

r)1(year)

1(year)

6(hou

r)6(hou

r)Ba

ttery

type

Rechargeablerem

ovable

Rechargeable

Coinbatte

ryCoinbatte

ryRe

chargeable

Rechargeable

Network

BLE

BLE

BLE

Bluetooth20

BLE

Datap

rocessing

Itimes

timestimes

timestimes

I2CEx

tension

Itimes

timestimes

timestimes

Sensors

Acceleratio

nI

II

II

Gyro

II

timesI

IMagnetic

II

II

IAirpressure

II

II

ITemperature

II

Itimes

IHum

idity

II

Itimes

timesLight

II

Itimes

timesUV

Itimes

Itimes

timesMi c

timesI

timestimes

times

Journal of Sensors 5

Flash memory(back side)

50GG

60GB

Temperature amphumidity(SHT-20)

UV(VEML6070)

Pressure(LPS25HB)

Light(BH1780GLI)

Charger(BQ24090)

Micro USB

Li-Po battery

USBUART(CP2104)

9-axis(MPU-9250)

BLE(nRF51822)

Figure 2 Hardware of SenStick 2 (8 sensors BLE flash memory etc)

Table 2 Sensors embedded in SenStick 23

Type of sensor Model number Power consumption9-Axis MPU-9250 280 120583Asim37mAPressure LPS25HBTR 4 120583Asim25 120583ATemperaturehumidity SHT20 270 120583Asim330 120583AIlluminance BH1780GLI-E2 120120583Asim200 120583AUltraviolet (UV) VEML6070 100 120583Asim250 120583A

times 5mm (119863) and its weight is around 3 (g) including thebattery On this tiny board eight sensors (acceleration gyromagnetic light UV temperature humidity and pressure)flash memory (32Mbytes) BLE battery and charging circuitare densely embedded as shown in Figure 2 The modelnumber and power consumption of the sensors embedded inSenStick are listed in Table 2 All the sensors are popular chipsused worldwide and their performances are ensured by eachmanufacturer

The novelty of our hardware is the design of the sensorboard composed of the elaborated combination to satisfythe requirements presented in Section 2 This contributionbreaks down into the following aspects

(1) Combination of BLE and Flash Memory Almost allthe BLE-based devices equipped no large flash memoryto record sensing data because these devices are designedon the premise that sensing data are always recorded onsmartphones connected via BLE However the transmissionspeed of BLE is not enough for sending all the sensing dataof multiple sensors with the high sampling frequency As aresult some data will be dropped in the transmission Sincethe design of existing BLE-based devices is not appropriatefor research purpose we designed a new BLE-based sensingboard with a large flash memory which allows users torecord all the sensing data precisely Also it means that it canwork without holding a smartphone This ability is useful forsensing themotion of animals that cannot have a smartphone

(2) Selection of Powerful and Energy-Saving SoC Weselected Nordicrsquos nRF52832 SoC as SenStickrsquos microcon-troller Because the nRF52832 SoC is built around a 32-bit ARM Cortex-M4F CPU with 512 kB + 64 kB RAM itis extremely powerful Moreover the embedded 24GHztransceiver supports Bluetooth low energy and has extremelylow power consumption and it is the worldrsquos smallest classmodule Each sensor is connected via two I2C buses inde-pendently processing high-speed sensor (acceleration gyroandmagnetic) and low-speed sensor (light UV temperaturehumidity and pressure) taking into account the differencein bus occupancy time This makes it possible to realize highsampling with the motion sensors

(3) Application-Friendly Battery Type and Shape Since Sen-Stick is designed to bemounted on objects such as chopstickstoothbrushes and glasses it is desirable that the shape is thinand compact However sensors such as SensorTag equippedwith readily available coin batteries are inappropriate inshape because the footprint is square Also in the researchnonrechargeable coin batteries are costly and troublesomeFor those reasons we selected a thin and compact lithiumpolymer battery on SenStick This type of battery was harderto obtain than expected Therefore the procurement ofbattery has become the bottleneck of development

SenStickrsquos firmware is newly designed and developed byconsidering aforementioned requirements Generic Attribute(GATT) profile of SenStick provided by firmware is shownin Figure 3 The GATT server in SenStick provides threeservices (SenStick Control Service Metadata Read Serviceand Sensor Service) The SenStick Control Service providesoperation instructions such as starting and stopping sensingand logging The metadata reading service provides thefunction of reading metainformation of log data The SensorService provides functions to specify the sensing and loggingoperation of each sensor (0 acceleration 1 gyro 2 magnetic3 light 4 UV 5 temperaturehumidity 6 pressure) Also itprovides real-time sensing data reading function and log datareading function

6 Journal of Sensors

(32-)

Peripheral

GATT server

SenStick Control Service

Metadata Read Service

Read-write

Data notication

Flashmemory

(0 acceleration 1 gyro 2 magnetic 3 light

4 UV 5 temperaturehumidity 6 pressure)

GATT client

Central

~Sensor Service (0 6)

Figure 3 Generic Attribute (GATT) profile of SenStick

Table 3 BLE communication specification

Type of content SpecAdvertising packet Complete UUID flagScan response Local name (SenStick)Advertising packet interval 100ms (30 s)rarr1000ms (150 s)rarrsleepConnection interval max 80msmin 20msSupervision timeout 4000msSlave latency 0

Basically all the sensing data will be notified to a smart-phone in real time up to a transmission capacity of BLE Ifthe amount of sensing data exceeds the transmission capacitya part of sensing data will be dropped Therefore the real-time sensing data reading function is used formonitoring thestatus or for an interactive application that does not requireprecise data For precise data acquisition the log data readingfunction which guarantees the data reading order is usedThe communication specifications of BLE are described inTable 3

As shown in Table 4 the fixed capacities of a flashmemory are allocated to each sensorThe number of samplesindicates the total number of samples that can be recorded bythe allocated capacities

SenStick stops logging when the number of samples ofone sensor exceeds the storage capacity allocated for it Themaximum recording time of acceleration gyroscope andmagnetic sensor is 4 hours 40 minutes at sampling intervalof 10 milliseconds (sampling frequency 100Hz) and 14 hours10 minutes at sampling period of 30 milliseconds (samplingfrequency 33Hz) The maximum recording time of othersensors is 9 hours 26 minutes at sampling period of 200milliseconds (sampling frequency 5Hz)

32 Software We provide mobile applications for iOS andAndroid and a library for nodejs platform Common func-tions of them are operating function parameter settingfunction and real-time data view function An additional

Table 4 Allocated storage capacity per sensor

Amount of allocatedstorage capacity Samples

Acceleration 102Mbytes 17M samplesGyro 102Mbytes 17M samplesMagnetic 102Mbytes 17M samplesIlluminance 340KB 170K samplesUV (UV) 340KB 170K samplesHumidity 170KB 170K samplesAir temperature 170KB 170K samples

function of iOS application is a firmware update functionThose of Android application and nodejs applications areground truth video recording function and an ability to con-nect multiple SenSticks respectively Since all these softwareapplications are shared on GitHub as an own source softwareall the users can create their application based on their ownpurposes

Communication Protocol Here we explain the communica-tion protocol between SenStick and softwareThe explanationis based on ldquonode-SenStickrdquo which is our developed libraryfor nodejs Figure 4 shows a protocol sequence betweenSenStick and the central terminal (node-SenStick on Mac)

(I) Discovery First of all the application needs to find thesurrounding SenStick by using SenStickdiscovery() methodin our library When SenStickdiscovery() method finds aSenStick a callback function with an instance of SenStickobject containing the SenStickrsquos UUID and LocalName asarguments is called

(II) Connect If the target SenStick is found by the discoveryprocess the application is going to establish a connec-tion with it by calling SenStickprototypeconnectAndSetUp()method After establishing the connection this methodacquires the information necessary for operation from thetarget SenStick

Journal of Sensors 7

Central

Node-SenStick SenStick

SenStickdiscovery()

SenStickprototypeconnectAndSetUp()

Establish connection

SenStickprototypewriteSensorMeasurementCong()

Reect setting

SenStickprototypestartSensingAndLogging()

SenStickprototypenotifySensor()Start sensing

Select sensor to notify

Sensing data

SenStickprototypestopSensingAndLogging()

Stop sensing

Enable log notication

Select log data to notify

SenStickprototypewriteSensorLogReadoutTargetID()

Logging data

SenStickprototypedisconnect()

Disconnect

SenStickprototypenotifySensorLogData()

Periperal

(I) Discovery

(II) Connect

(III) Setting

(V) Obtaining log

(VI) Disconnect

(IV) Start sensingamp

RT receiving

Figure 4 Communication sequence between SenStick and node-SenStick

(III) Setting After establishing the connection the appli-cation sets three parameters (operation mode samplinginterval and measurement range) for each sensor by usingSenStickprototypewriteSensorMeasurementConfig()methodThe operation mode is a parameter that sets valida-tioninvalidation of sensing and logging of each sensor Thesampling interval and measurement range are parameters ofeach sensorwhichwill be changed by the sensing purpose andapplications

(IV) Start Sensing and Real-Time Data Receiving When Sen-StickprototypestartSensingAndLogging() method is calledSenStick starts sensing and logging Only valid sensorsrsquodata are transmitted via BLE and also recorded into aflash memory ID of each log is automatically assignedDuring sensing SenStick notifies sensing data in realtime To receive this notification the application callsSenStickprototypenotifySensor() method (Sensor representseach sensor name) and activates the notification for

8 Journal of Sensors

(i) Frequency(ii) Range

Device select Main view Settings Firmware update

Real-time view Log list in SenStick Download from SenStick

Selected sensorSensor name

Sensing frequency

Range of y-axis

Figure 5 iOS application for SenStick

the sensor When a notification of sensing data isreceived sensorChange event will be issued If Sen-StickprototypestopSensingAndLogging() method is calledSenStick stops sensing and logging

(V) Log Data Reading To read the log data recordedin the flash memory to the application the applicationcalls SenStickprototypenotifySensorLogData()method (Sen-sor represents each sensor name) and enables log datanotification If the application specifies a valid log ID bycalling SenStickprototypewriteSensorLogReadoutTargetID()method SenStick starts informing the sensing data recordedin the log sequentially To receive notification of log datasensorLogDataReceived event will be issued By receiving thisnotification by the application it is possible to acquire the logdata recorded in the memory

(VI) Disconnect After finishing the data acquisition Sen-Stickprototypedisconnect() method will be called to releasethe connection of SenStick And SenStick will begin advertis-ing again

User InterfaceWe explain the user interface of the applicationbased on iOS version Figure 5 shows a sophisticated mobileapplication for SenStick As shown in Figure 5 the userselects target SenStick from devices listThen the applicationshows the main view In the main view the user can selectsensors to be acquired For immediate data monitoring allthe received data via BLE can be shown in the graph view inreal time The application can control all the settings of eachsensor such as sample frequency and sensing range And alsoit has firmware update function Sensing data stored in theflash memory of SenStick is viewed from log list and it can be

downloaded to the smartphone in CSV formatThis functionprevents data loss caused by BLE communication

33 3D Case Data In order to make sensorized things anattachment case for mounting the SenStick is required Wedesigned the basic 3D case of SenStick By extending thisbasic case users can design derivative cases for chopsticksglasses and so on Actually our laboratory has succeeded indesigning cases for several things as shown in Figure 6 Allof these cases data are shared on our community site Eachcase is designed in a few minutes thanks to the basic caseFor example SenStick case for glasses is made by combiningbasic case of SenStick and open 3D data of glasses distributedby i Design Studio In addition due to the popularizationof 3D printers in the world and enhancement of tools andreferences for 3D modeling all the users can print out theirdesired SenStick cases by themselves and attach our sensor toeverything

4 Case Studies of SenStick

SenStick an ultra small sensor can be embedded into varioustiny things in our daily life Our first target is our dailybelongings such as glasses chopsticks and pens becausesensorization of daily belongings is good to intuitively under-standwhat can be sensed andwhich sensor is usefuleffectiveSenStick can upgrade normal glasses to intelligent oneswhichcan monitor a head movement posture and the amount ofsunlight received If SenStick is embedded into chopsticksand pens we can observe eating activities and handwritingactivities respectively In the future we will embed it into astick for elderlymonitoring sports gearsmusical instrument

Journal of Sensors 9

Chopsticks Tooth brush Glasses Umbrella Name card Cookware

A user can design original case data in a few minutes

Basic 3D case of SenStick

Figure 6 3D case data of SenStick

Sensorized

Chopsticks

Figure 7 Eating activity recognition by sensorized chopsticks

kitchen utensils animals stuffed toy a remote control andany kind of things that can be sensing targets In this sectionwe introduce some case studies results utilizing SenStick

41 Eating Behavior Recognition by Sensorized Chopsticks Inmany cases East Asian people eat meals with chopsticksBy analyzing the data obtained from chopsticks there is apossibility that we can estimate the eating habits includinganswers for questions like ldquohow did you eatrdquo and ldquowhatdid you eatrdquo and so forth These data are very useful formaking healthcare applications and leading a healthy lifeThis is one reason why we develop the sensorized chopsticksusing SenStick shown in Figure 7

Research goals of the eating behavior recognition by sen-sorized chopsticks are as follows G1 (behavior) recognizingthe timing and speed and detecting ldquocroprdquo and ldquointakerdquoaction G2 (food type) classifying food content like ricenoodles and so forth from the eating behavior and G3(person) identifying an individual from using chopsticks Inthis paper we show the research results on G1 and G2

We conducted basic experiments to recognize the eatingbehavior (G1 and G2) We used acceleration and gyroscopedata measured by SenStick where the sensing rate is 10HzThe purpose of experiment 1 is to understand the basic

characteristics of sensing data from sensorized chopstickwhen a participant ate chips The purpose of experiment 2 isto identify the food type from the difference in movement ofchopsticks We assumed 4 classes (C1 rice and dish C2 misosoup C3 noodles and C4 shrimp) as food types

Figure 8 shows time series of sensing data for a set ofeating actions in experiment 1 The eating actions are cropaction and intake action In crop action 6-axis motionsensors detect movement (open and close) of the chopsticksIn intake action sensors detect movement of the arm fromthe dish to the mouth Each graph in Figure 8 shows certaincorrelation with both actions

Figure 9 shows a typical signal for each food in exper-iment 2 In this experiment we calculate the recognitionaccuracy of food type classification by using Random Forestclassier ofWeka Tables 5 and 6 show the classification resultsThe average recognition accuracy was 896 If we did nothave a SenStick it would be hard to develop the sensorizedchopsticks that can be used in the actual environment

42 Daily Activity Monitoring by a Sensorized Belt Recentlycorpulent people are rapidly increasing and about 700 mil-lion people belong to the obesity group [18] Nowadays manyoffice workers use computers heavily and their sitting time

10 Journal of Sensors

Step 1 crop action Step 2 intake action

Lower

Upper

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

X

Y

Z

Synthetic

X

Y

Z

Synthetic

X

Y

Z

Synthetic

X

Y

Z

Synthetic

minus250

minus150

minus50

50

150

250

Ang

ular

velo

city

(rad

s)

minus20

minus10

01020

Acce

lera

tion

(ms

2)

minus250

minus150

minus50

50

150

250

Ang

ular

velo

city

(rad

s)

minus20

minus10

01020

Acce

lera

tion

(ms

2)

Open1 Close2 Raise3 Eat4 Drop5

1 2 3 4 5

Figure 8 Time series of sensing data in eating activity

in office increases Keeping incorrect postures during longworking time increases the risk of obesity of office workersTherefore it is necessary tomonitor the daily activity of officeworkers for correcting the bad habits

Motivated by this we have developed a sensorized beltcalled Waiston Belt [8] which aims to monitor a userrsquosdaily activities for maintaining health As products of belt-type wearable devices Welt [19] and Belty [20] are on saleThese provide the function of waist measurement activity

tracking and step counting However API for obtainingsensing data is not provided and it is not suitable for researchpurposes Generally it is hard for ordinary researchers toembed sensors battery BLE and vibrator into a small buckle-size case that can be worn in everyday life Nam et al [21]have proposed a belt-attached device for monitoring postureHowever the device has not reached a sufficient level to beused on a daily basis because of exposed wiring and uncoolappearance However by extending SenStick we succeeded

Journal of Sensors 11

C1

C2 C3

C4

Typical signal of each food

C1 rice amp dish C2 miso soup C3 noodles C4 shrimp

400

200

0

minus200

minus400

Figure 9 Eating activity recognition by sensorized chopsticks

SenStick

Waiston Belt

Figure 10 Waiston Belt based on SenStick

Table 5 Experimental result (food type)

Food type Number ofpieces of data Precision Recall 119865-Measure

C1 rice and dish 3011 0894 0945 0919C2 miso soup 2187 0907 0841 0873C3 noodles 2761 088 0896 0888C4 shrimp 2091 0907 088 0893

0896 0896 0895

to develop a stylish prototype of Waiston Belt shown inFigure 10 quickly Since the battery life of SenStick is 15 hourswith a small battery (60mAh) we can sense continuously inour daily lives As shown in Figure 11 it is possible to estimatevarious usersrsquo contexts (How much did you walk Are youusing the stairs Is posture good at work) by analyzing timeseries of sensing data obtained from the sensorized belt

An important point in this use case is that usersrsquo contextscan be estimated by combining multiple sensing data For

Table 6 Confusion matrix of food type recognition

PredictedC1 C2 C3 C4

ActualC1 rice and dish 094 004 001 000C2 miso soup 012 084 004 000C3 noodles 002 002 090 006C4 shrimp 001 001 011 088

example climbing stairs is estimated from the changes ofacceleration and atmospheric pressure The change of thehumidity data will be helpful to detect that a user enteredan air-conditioned room A SenStick makes it possible toeasily carry out research on behavior recognition with acombination of such multiple sensors

We introduce basic experiments about the posture recog-nition during deskwork by using this beltThe purpose of thisexperiment is to identify the posture type from the attitude

12 Journal of Sensors

Enter the lab

HumidityTemp

Humidity dropping

Pressure dropping

Working

Walking WalkingClimbing stairs Sitting (good posture) Sitting (bad posture)

AccsXAccsYAccsZ

60

40

20

1003

1002

1001

1000

999

25

2

15

1

05

0

minus15

minus1

minus05

Air pressure

Figure 11 Time series of sensing data in daily activity

C1 good posture C2 bad posture 1

C3 bad posture 2 C4 bad posture 3

Figure 12 Time series of sensing data in posture monitoring

angle of the belt calculated from 3 axial acceleration sensorsWe set the sensing rate as 10Hz and collected 6000 samples(10min) for each posture We assumed 4 classes (C1 goodposture C2 bad posture 1 C3 bad posture 2 and C4 badposture 3) as shown in Figure 12

In this experiment we calculated the recognition accu-racy of posture type classification by using Random Forestclassifier of Weka Tables 7 and 8 show the classificationresults The average recognition accuracy was 997 Inthe future based on the result of posture estimation we

Journal of Sensors 13

Sensorized glasses

Figure 13 Sensorized glasses

Table 7 Experimental result (posture type)

Posture type Number ofpieces of data Precision Recall 119865-Measure

C1 good posture 6000 1000 1000 1000C2 bad posture 1 6000 0994 0994 0994C3 bad posture 2 6000 0994 0994 0994C4 bad posture 3 6000 0999 0999 0999

0997 0997 0997

will develop a software application that suggests correctinghisher bad posture to a right posture during desk work

43 Indoor Localization by Sensorized Glasses We have pro-posed an indoor localization system using illuminance-basedtrilateration method [22] In this system we assume that theperson wears glasses having an illuminance sensor as shownin Figure 13 and controllable light sources are deployed in thetarget environment Our system controls lights andmeasuresthe variation of illuminance at the glasses As shown inFigure 14 the distances (1198891ndash1198893) from each light (1198971ndash1198973) to theuser (119906) are estimated from the distance-illumination modelconstructed in advance and userrsquos position is estimated basedon trilateration method using estimated distances

As a result of an experiment where three controllablelight sources were deployed in a room we confirmed thatour system could estimate the position within 05m erroron average By using SenStick we could easily develop thesensorized glasses equipped with illuminance sensor

44 Sensorized Airsoft Gun We have developed the sen-sorized airsoft gun for activity recognition in airsoft gameas shown in Figure 15 Airsoft game is a sport in whichparticipants eliminate opponents by hitting each other usingreplica weapons called airsoft guns The graph of a typicalsignal from the sensorized airsoft gun is shown in Figure 16A user can count the number of shots of a ball by attachingSenStick to his airsoft gun

45 Another Example Studentrsquos Unique Achievements andSurvey Results SenStick is also used in a lecture of IoT

Table 8 Confusion matrix of posture type recognition

PredictedC1 C2 C3 C4

ActualC1 good posture 1000 0000 0000 0000C2 bad posture 1 0000 0994 0006 0001C3 bad posture 2 0000 0006 0994 0000C4 bad posture 3 0000 0001 0000 0999

prototyping Students realize their unique IoT devices easilyby using SenSticks Here we introduce some of the developedIoT devices and survey results about the usefulness ofSenStick answered by students

Figure 17 shows a sensorized pencil with two SenSticksThe sensorized pencil is developed to recognize the charac-ters written by this pencil Even though two SenSticks areembedded to detect precise movement of pencil the size ofthe pencil is reasonable

Figure 18 shows a sensorized name tag anddigital signageSince we changed the battery from 60mAh to 900mAh thetag keeps running more than one week It aims to recordemployeesrsquo or studentsrsquo daily activities to support their healthThe digital signage informs the user who wears a sensorizedname tag about the userrsquos activity data when the user standsin front of it

Figure 19 shows a sensorized spoon used in the digitalfish catching game for children In this game the sensorizedspoon detects the scooping movement of the hand Thuschildren can catch the digital fish displayed by the projectionmapping by using sensorized spoonThis systemwas actuallyused at the universityrsquos open campus event and around100 children enjoyed this game and showed interest in IoTtechnology

The survey results obtained from the ten graduate stu-dents (ninemales and one female) who developed prototypesas mentioned above are shown in Table 9 A questionnaireis a five-grade form (5 strongly agree 4 agree a little 3neither agree nor disagree 2 disagree a little and 1 stronglydisagree) From the results of questions 1 and 2 manyparticipants think that SenStick platform is useful for IoT

14 Journal of Sensors

Position

Distance illumination model

Using trilateration method

a

Light 3

Light 2

Light 1

L = minus421d + 1496

L = minus063d + 501

05 1 15 2 25 3 35 4 450Distance between the illuminance sensor and the lighting device (m)

02468

1012141618

Illum

inan

ce v

alue

(lux

)

Distanceu

d1

d2

d3

l1 p1 x1

l3 p3 x3

l2 p2 x2

Figure 14 Indoor localization by sensorized glasses

Sensorized airsoft gun

Figure 15 Sensorized airsoft gun

research and IoT education Also the results of question 3show that many students are interested in the SenStick andwant to use it again From the results of questions 4 and 5we can see that usability and specification of SenStick arerelatively high There were opinions about the usefulness ofSenStick platform such as ldquoI could quickly try out ideas of IoTprototypingrdquo ldquoIt was easy to collect sensing datardquo and ldquoSinceSenStick is small it was easily attached tomost of the thingsrdquoOn the other hand there were opinions about improvements

such as ldquoI want to use higher sensing frequency (50ndash100Hz)rdquoldquoI want to process a sensing data on SenStickrdquo and ldquoI want tomodify the firmware itself depending on the application ofthe IoT devicerdquo

5 Conclusion

In this paper we have introduced a smart and comprehensivesensing platform called SenStick which is composed of

Journal of Sensors 15

Typical signal of airso gun

Hold a gun Ready

Shoot

1 2 3 4 5 6 70

00

30

60

91

21

51

82

12

42

73

03

33

63

94

24

54

85

15

45

76

06

36

66

97

27

57

88

18

48

79

09

39

69

910

210

510

811

111

411

712

012

312

612

913

2

minus600

minus400

minus200

0

200

400

600

800

acc_xacc_yacc_z

gyro_xgyro_y

acc_synthesis gyro_zgyro_synthesis

Figure 16 Time series of sensing data in shooting an airsoft gun

Sensorized pencil

Figure 17 Sensorized pencil

Sensorized name tagVisualization of sensor data

Figure 18 Sensorized name tag

Sensorized spoon Digital fishing game

Figure 19 Sensorized spoon

Table 9 Questionnaire results about SenStick

Question contents ScoreQ1 Do you think that SenStick will be useful for IoTresearch 49

Q2 Do you think that SenStick will be useful for IoTeducation 47

Q3 Do you want to use SenStick again 49Q4 Do you think that SenStick was easy to use 41Q5 Are you satisfied with specification of SenStick 41

hardware software and 3D case data The main purpose ofour platform is to simplify the sensorization of our personalbelongings

The hardware of SenStick is maybe one of the worldrsquossmallest sensor boards which includes 8 kinds of sensorsand BLE In addition to the hardware we also developedsupport software for managing monitoring recording andviewing the hardware By providing 3D case data all theresearchers can measure every tiny activity in daily life easilyand efficiently For an educational use it is also useful forunderstanding sensors and prototyping the desired systemincluding tiny censored things

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

This work is supported in part by JST PRESTO and JSPSKAKENHI (Grant no 17J10021) and ldquoResearch and Devel-opment of Innovative Network Technologies to Create theFuturerdquo the Commissioned Research of National Instituteof Information and Communications Technology (NICT)Japan

16 Journal of Sensors

References

[1] O Brdiczka M Langet J Maisonnasse and J L CrowleyldquoDetecting human behavior models from multimodal observa-tion in a smart homerdquo IEEE Transactions on Automation Scienceand Engineering vol 6 no 4 pp 588ndash597 2009

[2] J Chen A Kam J Zhang N Liu and L Shue ldquoBathroomactivity monitoring based on soundrdquo in Pervasive ComputingThird International Conference PERVASIVE 2005 MunichGermanyMay 8-13 2005 Proceedings vol 3468 ofLectureNotesin Computer Science pp 47ndash61 Springer Berlin Germany2002

[3] G Bauer K Stockinger and P Lukowicz ldquoRecognizing theUse-Mode of Kitchen Appliances from Their Current Consump-tionrdquo in Smart Sensing and Context vol 5741 of Lecture Notesin Computer Science pp 163ndash176 Springer Berlin HeidelbergBerlin Heidelberg 2009

[4] K Ouchi and M Doi ldquoIndoor-outdoor activity recognition bya smartphonerdquo in Proceedings of the the 2012 ACM Conferencep 537 Pittsburgh Pennsylvania September 2012

[5] A Anjum and M U Ilyas ldquoActivity recognition using smart-phone sensorsrdquo in Proceedings of the IEEE 10th ConsumerCommunications and Networking Conference (CCNC rsquo13) pp914ndash919 IEEE January 2013

[6] O D Lara and M A Labrador ldquoA survey on human activityrecognition using wearable sensorsrdquo IEEE CommunicationsSurveys amp Tutorials vol 15 no 3 pp 1192ndash1209 2013

[7] ldquoJins memerdquo httpsjins-memecom[8] Y Matsuda T Ishioka T Hasegawa et al ldquoWaistonBelt A belt

for monitoring your real abdominal circumference foreverrdquo inProceedings of the ACM International Joint Conference on Perva-sive and Ubiquitous Computing and the 2015 ACM InternationalSymposium on Wearable Computers UbiComp and ISWC 2015pp 329ndash332 September 2015

[9] M Kurahashi K Murao T Terada and M Tsukamoto ldquoPer-sonal identification system based on rotation of toilet paperrollsrdquo in Proceedings of the IEEE International Conference onPervasive Computing and CommunicationsWorkshops (PerComWorkshops rsquo17) pp 521ndash526 IEEE Kona Hawaii USA March2017

[10] ldquoHapiforkrdquo httpswwwhapicom[11] Y Arakawa ldquoSenStick Sensorize every thingsrdquo in Proceedings of

the ACM International Joint Conference on Pervasive and Ubiq-uitous Computing and the 2015 ACM International SymposiumonWearable Computers UbiComp and ISWC 2015 pp 349ndash352September 2015

[12] Y Nakamura T Kanehira Y Arakawa and K YasumotoldquoSenStick 2 Ultra tiny all-in-one sensor with wireless chargingrdquoinProceedings of the 2016ACM International Joint Conference onPervasive and Ubiquitous Computing UbiComp 2016 pp 337ndash340 September 2016

[13] ldquoSensortag cc2541 (texas instruments)rdquo httpwwwticomtoolcc2541dk-sensor

[14] ldquoIot smart modulerdquo httpwwwalpscomjiotsmart[15] ldquoTsnd121 (atr-promotions)rdquo httpwwwatr-pcomproducts

TSND121html[16] ldquoWax9 (axivity)rdquo httpaxivitycomproductwax9[17] Uk biobank ldquoUk biobankrdquo httpwwwukbiobankacuk[18] J C Seidell and J Halberstadt ldquoThe global burden of obesity

and the challenges of preventionrdquo Annals of Nutrition andMetabolism vol 66 supplement 2 pp 7ndash12 2015

[19] ldquoWeltThe smart belt for fashion and healthrdquo httpswwwwelt-corpcom

[20] ldquoBelty power The belt your really deserverdquo httpswwwbeltyco

[21] H Nam J-H Kim and J-I Kim ldquoSmart Belt A wearabledevice for managing abdominal obesityrdquo in Proceedings of theInternational Conference on Big Data and Smart ComputingBigComp 2016 pp 430ndash434 January 2016

[22] K Moriya M Fujimoto Y Arakawa and K Yasumoto ldquoIndoorlocalization based on distance-illuminance model and activecontrol of lighting devicesrdquo in Proceedings of the 2016 Interna-tional Conference on Indoor Positioning and Indoor NavigationIPIN 2016 pp 1ndash6 October 2016

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal of

Volume 201

Submit your manuscripts athttpswwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

Page 3: SenStick: Comprehensive Sensing Platform with an Ultra ...downloads.hindawi.com/journals/js/2017/6308302.pdf · ResearchArticle SenStick: Comprehensive Sensing Platform with an Ultra

Journal of Sensors 3

2 but the board is unified into one board again like SenStick1 The main change is the upgrade of BLE chip from nRF51 tonRF52 Since the processing power is increased we can addsome calculation process on the board

In the software part support application is developedfor mobile devices (iOS and Android) and desktop PC(LinuxmacOS andWindows) Each application canmonitorand record sensing data synchronously and can configurethe parameters of each sensor The Android version cansimultaneously record a ground truth video and sensing dataThe PC version allows a user to connect multiple SenStickssimultaneously and also provides a function to use Node-RED which provides a simple programming interface

In 3D case part we designed the data of basic 3D case ofSenStick It is possible to easily design the 3D case for variousthings by using our base 3D case data

The remaining part of the paper is organized as followsWe first introduce various existing products and comparethem with SenStick in Section 2 Next we explain the detailsof our proposed platform SenStick in Section 3 In Section 4we introduce some case studies utilizing SenStick and somesurvey results answered by students who actually used Sen-Stick platform Finally we conclude the paper in Section 5

2 Related Work

We summarize competitive sensors in the market in Table 1SensorTag [13] of Texas Instruments is the most competi-

tive sensor board similar to SenStick The sensors embeddedon SensorTag are almost the same as SenStick except for UVsensor However SensorTag does not have stand-alone datarecording function and a flash memory because this sensoris designed on the premise that sensing data are always storedon the smartphone connected by BLE Since the main targetof SensorTag is environmental sensing the sensing frequencyof motion sensors is limited up to 1Hz and a rechargeablebattery is adopted Therefore it is not suitable for activitysensing of IoT research In addition the board shape size andweight are completely different SenStick is much smaller andslimmer

IoT Smart Module [14] is a small sensor board equippedwith several sensors like SensorTag Support applicationsprovide functions to cooperate with IBM Bluemix so itis possible to smoothly perform tasks from sensing ofdata to cloud analysis However support applications onlywork on Android Similar to SensorTag of Ti it adoptsnonrechargeable coin battery Although it can set highersensing frequency a user must change the coin battery atshort intervals Also logically it cannot transmit all thesensing data in case of higher sensing frequency becausethe transmission capacity of BLE is low Therefore it is notsuitable for research that requires precise data

TSND121 [15] of ATR-Promotions is another competitivesensor because it was used in some academic research Ithas 9-axis motion sensors and ambient pressure sensor andadopts Classic Bluetooth 20 for transmitting data Becauseit only focuses on monitoring activities of a human and arobot the size of the sensor is relatively large but at most7 sensors can be simultaneously connected to a control PC

Since it always requires a PC for recording sensed data it isnot suitable for sensing our daily life out of the lab

WAX9 [16] of Axivity used in UK Biobank [17] was astreaming inertialmeasurement unit (IMU)which has a largeflash memory for logging The sensor combines a MEMSaccelerometer gyro and magnetometer with a Bluetoothlow energy (BLE) compatible radio Additionally WAX9 alsofeatures a barometric pressure sensor and temperature sensorHowever it does not have some environmental sensors suchas humidity light and UV sensor and always only distributesthe Windows-compatible software for data collection andvisualization It has already been discontinued

Based on the features of other competing sensors wesummarize the requirements for SenStick platform

(1) Hardware Requirements The SenStick platform aims tosense tiny activities that cannot be sensed by existing smart-phones and wearable devices Therefore essential functionsmust be embedded in a small sensor board and each sensormust collect data in high sampling rate Since BLE commu-nication dose not guarantee reliable data communicationit is necessary to be able to record in standalone withoutrecording devices such as a smartphone In addition it isrequired that the SenStick must have rechargeable batterysystem because a coin battery is not suitable for frequent use

(2) Software Requirements The SenStick platform aims tosimplify the data collection and analysis from sensorizedthings Therefore support software should be multiplatformIt should be compatible with the various operating systemsSupport software requires not only basic function such as datarecording but also additional functions such as simultaneousrecording of sensing data frommultiple SenSticks and simpleprogramming interface for data processing

(3) 3D Case Requirements The SenStick platform aims topromote the sensorization of everything For this purposeit is desirable to provide a 3D case of SenStick that can beattached to various belongings Further 3D cases createdby someone should be shared with other users through ourwebsite

3 Comprehensive Sensing Platform forIoT Research SenStick

Our proposed platform called SenStick consists of 3 com-ponents hardware software and 3D case In the followingsubsections we explain the details of these components Thisplatform allows a user to sensorize everything easily andefficiently We believe that it supports the rapid prototypingby researchers and is useful for IoT education (a videoillustrating SenStickrsquos hardware software and 3D case datais provided in Supplementary Material available online athttpsdoiorg10115520176308302)

31 Hardware Aswe explained in Section 1 we have updatedthe hardware three times from 2015 The explanation in thefollowing part is based on SenStick 2 developed in 2016The size of the sensor board is 55mm (119882) times 10mm (119867)

4 Journal of Sensors

Table1Com

paris

onof

competitives

ensors

SenS

tick

3Se

nStic

k 2

Sens

orTa

g (C

C265

0)Io

T Sm

art M

odul

eTS

ND

121

WA

X9

Size

(mm)

50(119882

)times10

(119867)times

10(119863

)50

(119882)times

10(119867

)times10

(119863)

42(119882

)times32

(119867)times

10(119863

)44

(119882)times

27(119867

)times11(119863

)37

(119882)times

46(119867

)times12

(119863)23

(119882)times

325(119867

)times76

(119863)

Weight(g)

35

28mdash

227

Stand-alon

erecording

Itimes

timestimes

ISm

artpho

neapp

II

Itimes

timesDesktop

app

I998779

timesI

IBa

ttery

life

15(hou

r)1(year)

1(year)

6(hou

r)6(hou

r)Ba

ttery

type

Rechargeablerem

ovable

Rechargeable

Coinbatte

ryCoinbatte

ryRe

chargeable

Rechargeable

Network

BLE

BLE

BLE

Bluetooth20

BLE

Datap

rocessing

Itimes

timestimes

timestimes

I2CEx

tension

Itimes

timestimes

timestimes

Sensors

Acceleratio

nI

II

II

Gyro

II

timesI

IMagnetic

II

II

IAirpressure

II

II

ITemperature

II

Itimes

IHum

idity

II

Itimes

timesLight

II

Itimes

timesUV

Itimes

Itimes

timesMi c

timesI

timestimes

times

Journal of Sensors 5

Flash memory(back side)

50GG

60GB

Temperature amphumidity(SHT-20)

UV(VEML6070)

Pressure(LPS25HB)

Light(BH1780GLI)

Charger(BQ24090)

Micro USB

Li-Po battery

USBUART(CP2104)

9-axis(MPU-9250)

BLE(nRF51822)

Figure 2 Hardware of SenStick 2 (8 sensors BLE flash memory etc)

Table 2 Sensors embedded in SenStick 23

Type of sensor Model number Power consumption9-Axis MPU-9250 280 120583Asim37mAPressure LPS25HBTR 4 120583Asim25 120583ATemperaturehumidity SHT20 270 120583Asim330 120583AIlluminance BH1780GLI-E2 120120583Asim200 120583AUltraviolet (UV) VEML6070 100 120583Asim250 120583A

times 5mm (119863) and its weight is around 3 (g) including thebattery On this tiny board eight sensors (acceleration gyromagnetic light UV temperature humidity and pressure)flash memory (32Mbytes) BLE battery and charging circuitare densely embedded as shown in Figure 2 The modelnumber and power consumption of the sensors embedded inSenStick are listed in Table 2 All the sensors are popular chipsused worldwide and their performances are ensured by eachmanufacturer

The novelty of our hardware is the design of the sensorboard composed of the elaborated combination to satisfythe requirements presented in Section 2 This contributionbreaks down into the following aspects

(1) Combination of BLE and Flash Memory Almost allthe BLE-based devices equipped no large flash memoryto record sensing data because these devices are designedon the premise that sensing data are always recorded onsmartphones connected via BLE However the transmissionspeed of BLE is not enough for sending all the sensing dataof multiple sensors with the high sampling frequency As aresult some data will be dropped in the transmission Sincethe design of existing BLE-based devices is not appropriatefor research purpose we designed a new BLE-based sensingboard with a large flash memory which allows users torecord all the sensing data precisely Also it means that it canwork without holding a smartphone This ability is useful forsensing themotion of animals that cannot have a smartphone

(2) Selection of Powerful and Energy-Saving SoC Weselected Nordicrsquos nRF52832 SoC as SenStickrsquos microcon-troller Because the nRF52832 SoC is built around a 32-bit ARM Cortex-M4F CPU with 512 kB + 64 kB RAM itis extremely powerful Moreover the embedded 24GHztransceiver supports Bluetooth low energy and has extremelylow power consumption and it is the worldrsquos smallest classmodule Each sensor is connected via two I2C buses inde-pendently processing high-speed sensor (acceleration gyroandmagnetic) and low-speed sensor (light UV temperaturehumidity and pressure) taking into account the differencein bus occupancy time This makes it possible to realize highsampling with the motion sensors

(3) Application-Friendly Battery Type and Shape Since Sen-Stick is designed to bemounted on objects such as chopstickstoothbrushes and glasses it is desirable that the shape is thinand compact However sensors such as SensorTag equippedwith readily available coin batteries are inappropriate inshape because the footprint is square Also in the researchnonrechargeable coin batteries are costly and troublesomeFor those reasons we selected a thin and compact lithiumpolymer battery on SenStick This type of battery was harderto obtain than expected Therefore the procurement ofbattery has become the bottleneck of development

SenStickrsquos firmware is newly designed and developed byconsidering aforementioned requirements Generic Attribute(GATT) profile of SenStick provided by firmware is shownin Figure 3 The GATT server in SenStick provides threeservices (SenStick Control Service Metadata Read Serviceand Sensor Service) The SenStick Control Service providesoperation instructions such as starting and stopping sensingand logging The metadata reading service provides thefunction of reading metainformation of log data The SensorService provides functions to specify the sensing and loggingoperation of each sensor (0 acceleration 1 gyro 2 magnetic3 light 4 UV 5 temperaturehumidity 6 pressure) Also itprovides real-time sensing data reading function and log datareading function

6 Journal of Sensors

(32-)

Peripheral

GATT server

SenStick Control Service

Metadata Read Service

Read-write

Data notication

Flashmemory

(0 acceleration 1 gyro 2 magnetic 3 light

4 UV 5 temperaturehumidity 6 pressure)

GATT client

Central

~Sensor Service (0 6)

Figure 3 Generic Attribute (GATT) profile of SenStick

Table 3 BLE communication specification

Type of content SpecAdvertising packet Complete UUID flagScan response Local name (SenStick)Advertising packet interval 100ms (30 s)rarr1000ms (150 s)rarrsleepConnection interval max 80msmin 20msSupervision timeout 4000msSlave latency 0

Basically all the sensing data will be notified to a smart-phone in real time up to a transmission capacity of BLE Ifthe amount of sensing data exceeds the transmission capacitya part of sensing data will be dropped Therefore the real-time sensing data reading function is used formonitoring thestatus or for an interactive application that does not requireprecise data For precise data acquisition the log data readingfunction which guarantees the data reading order is usedThe communication specifications of BLE are described inTable 3

As shown in Table 4 the fixed capacities of a flashmemory are allocated to each sensorThe number of samplesindicates the total number of samples that can be recorded bythe allocated capacities

SenStick stops logging when the number of samples ofone sensor exceeds the storage capacity allocated for it Themaximum recording time of acceleration gyroscope andmagnetic sensor is 4 hours 40 minutes at sampling intervalof 10 milliseconds (sampling frequency 100Hz) and 14 hours10 minutes at sampling period of 30 milliseconds (samplingfrequency 33Hz) The maximum recording time of othersensors is 9 hours 26 minutes at sampling period of 200milliseconds (sampling frequency 5Hz)

32 Software We provide mobile applications for iOS andAndroid and a library for nodejs platform Common func-tions of them are operating function parameter settingfunction and real-time data view function An additional

Table 4 Allocated storage capacity per sensor

Amount of allocatedstorage capacity Samples

Acceleration 102Mbytes 17M samplesGyro 102Mbytes 17M samplesMagnetic 102Mbytes 17M samplesIlluminance 340KB 170K samplesUV (UV) 340KB 170K samplesHumidity 170KB 170K samplesAir temperature 170KB 170K samples

function of iOS application is a firmware update functionThose of Android application and nodejs applications areground truth video recording function and an ability to con-nect multiple SenSticks respectively Since all these softwareapplications are shared on GitHub as an own source softwareall the users can create their application based on their ownpurposes

Communication Protocol Here we explain the communica-tion protocol between SenStick and softwareThe explanationis based on ldquonode-SenStickrdquo which is our developed libraryfor nodejs Figure 4 shows a protocol sequence betweenSenStick and the central terminal (node-SenStick on Mac)

(I) Discovery First of all the application needs to find thesurrounding SenStick by using SenStickdiscovery() methodin our library When SenStickdiscovery() method finds aSenStick a callback function with an instance of SenStickobject containing the SenStickrsquos UUID and LocalName asarguments is called

(II) Connect If the target SenStick is found by the discoveryprocess the application is going to establish a connec-tion with it by calling SenStickprototypeconnectAndSetUp()method After establishing the connection this methodacquires the information necessary for operation from thetarget SenStick

Journal of Sensors 7

Central

Node-SenStick SenStick

SenStickdiscovery()

SenStickprototypeconnectAndSetUp()

Establish connection

SenStickprototypewriteSensorMeasurementCong()

Reect setting

SenStickprototypestartSensingAndLogging()

SenStickprototypenotifySensor()Start sensing

Select sensor to notify

Sensing data

SenStickprototypestopSensingAndLogging()

Stop sensing

Enable log notication

Select log data to notify

SenStickprototypewriteSensorLogReadoutTargetID()

Logging data

SenStickprototypedisconnect()

Disconnect

SenStickprototypenotifySensorLogData()

Periperal

(I) Discovery

(II) Connect

(III) Setting

(V) Obtaining log

(VI) Disconnect

(IV) Start sensingamp

RT receiving

Figure 4 Communication sequence between SenStick and node-SenStick

(III) Setting After establishing the connection the appli-cation sets three parameters (operation mode samplinginterval and measurement range) for each sensor by usingSenStickprototypewriteSensorMeasurementConfig()methodThe operation mode is a parameter that sets valida-tioninvalidation of sensing and logging of each sensor Thesampling interval and measurement range are parameters ofeach sensorwhichwill be changed by the sensing purpose andapplications

(IV) Start Sensing and Real-Time Data Receiving When Sen-StickprototypestartSensingAndLogging() method is calledSenStick starts sensing and logging Only valid sensorsrsquodata are transmitted via BLE and also recorded into aflash memory ID of each log is automatically assignedDuring sensing SenStick notifies sensing data in realtime To receive this notification the application callsSenStickprototypenotifySensor() method (Sensor representseach sensor name) and activates the notification for

8 Journal of Sensors

(i) Frequency(ii) Range

Device select Main view Settings Firmware update

Real-time view Log list in SenStick Download from SenStick

Selected sensorSensor name

Sensing frequency

Range of y-axis

Figure 5 iOS application for SenStick

the sensor When a notification of sensing data isreceived sensorChange event will be issued If Sen-StickprototypestopSensingAndLogging() method is calledSenStick stops sensing and logging

(V) Log Data Reading To read the log data recordedin the flash memory to the application the applicationcalls SenStickprototypenotifySensorLogData()method (Sen-sor represents each sensor name) and enables log datanotification If the application specifies a valid log ID bycalling SenStickprototypewriteSensorLogReadoutTargetID()method SenStick starts informing the sensing data recordedin the log sequentially To receive notification of log datasensorLogDataReceived event will be issued By receiving thisnotification by the application it is possible to acquire the logdata recorded in the memory

(VI) Disconnect After finishing the data acquisition Sen-Stickprototypedisconnect() method will be called to releasethe connection of SenStick And SenStick will begin advertis-ing again

User InterfaceWe explain the user interface of the applicationbased on iOS version Figure 5 shows a sophisticated mobileapplication for SenStick As shown in Figure 5 the userselects target SenStick from devices listThen the applicationshows the main view In the main view the user can selectsensors to be acquired For immediate data monitoring allthe received data via BLE can be shown in the graph view inreal time The application can control all the settings of eachsensor such as sample frequency and sensing range And alsoit has firmware update function Sensing data stored in theflash memory of SenStick is viewed from log list and it can be

downloaded to the smartphone in CSV formatThis functionprevents data loss caused by BLE communication

33 3D Case Data In order to make sensorized things anattachment case for mounting the SenStick is required Wedesigned the basic 3D case of SenStick By extending thisbasic case users can design derivative cases for chopsticksglasses and so on Actually our laboratory has succeeded indesigning cases for several things as shown in Figure 6 Allof these cases data are shared on our community site Eachcase is designed in a few minutes thanks to the basic caseFor example SenStick case for glasses is made by combiningbasic case of SenStick and open 3D data of glasses distributedby i Design Studio In addition due to the popularizationof 3D printers in the world and enhancement of tools andreferences for 3D modeling all the users can print out theirdesired SenStick cases by themselves and attach our sensor toeverything

4 Case Studies of SenStick

SenStick an ultra small sensor can be embedded into varioustiny things in our daily life Our first target is our dailybelongings such as glasses chopsticks and pens becausesensorization of daily belongings is good to intuitively under-standwhat can be sensed andwhich sensor is usefuleffectiveSenStick can upgrade normal glasses to intelligent oneswhichcan monitor a head movement posture and the amount ofsunlight received If SenStick is embedded into chopsticksand pens we can observe eating activities and handwritingactivities respectively In the future we will embed it into astick for elderlymonitoring sports gearsmusical instrument

Journal of Sensors 9

Chopsticks Tooth brush Glasses Umbrella Name card Cookware

A user can design original case data in a few minutes

Basic 3D case of SenStick

Figure 6 3D case data of SenStick

Sensorized

Chopsticks

Figure 7 Eating activity recognition by sensorized chopsticks

kitchen utensils animals stuffed toy a remote control andany kind of things that can be sensing targets In this sectionwe introduce some case studies results utilizing SenStick

41 Eating Behavior Recognition by Sensorized Chopsticks Inmany cases East Asian people eat meals with chopsticksBy analyzing the data obtained from chopsticks there is apossibility that we can estimate the eating habits includinganswers for questions like ldquohow did you eatrdquo and ldquowhatdid you eatrdquo and so forth These data are very useful formaking healthcare applications and leading a healthy lifeThis is one reason why we develop the sensorized chopsticksusing SenStick shown in Figure 7

Research goals of the eating behavior recognition by sen-sorized chopsticks are as follows G1 (behavior) recognizingthe timing and speed and detecting ldquocroprdquo and ldquointakerdquoaction G2 (food type) classifying food content like ricenoodles and so forth from the eating behavior and G3(person) identifying an individual from using chopsticks Inthis paper we show the research results on G1 and G2

We conducted basic experiments to recognize the eatingbehavior (G1 and G2) We used acceleration and gyroscopedata measured by SenStick where the sensing rate is 10HzThe purpose of experiment 1 is to understand the basic

characteristics of sensing data from sensorized chopstickwhen a participant ate chips The purpose of experiment 2 isto identify the food type from the difference in movement ofchopsticks We assumed 4 classes (C1 rice and dish C2 misosoup C3 noodles and C4 shrimp) as food types

Figure 8 shows time series of sensing data for a set ofeating actions in experiment 1 The eating actions are cropaction and intake action In crop action 6-axis motionsensors detect movement (open and close) of the chopsticksIn intake action sensors detect movement of the arm fromthe dish to the mouth Each graph in Figure 8 shows certaincorrelation with both actions

Figure 9 shows a typical signal for each food in exper-iment 2 In this experiment we calculate the recognitionaccuracy of food type classification by using Random Forestclassier ofWeka Tables 5 and 6 show the classification resultsThe average recognition accuracy was 896 If we did nothave a SenStick it would be hard to develop the sensorizedchopsticks that can be used in the actual environment

42 Daily Activity Monitoring by a Sensorized Belt Recentlycorpulent people are rapidly increasing and about 700 mil-lion people belong to the obesity group [18] Nowadays manyoffice workers use computers heavily and their sitting time

10 Journal of Sensors

Step 1 crop action Step 2 intake action

Lower

Upper

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

X

Y

Z

Synthetic

X

Y

Z

Synthetic

X

Y

Z

Synthetic

X

Y

Z

Synthetic

minus250

minus150

minus50

50

150

250

Ang

ular

velo

city

(rad

s)

minus20

minus10

01020

Acce

lera

tion

(ms

2)

minus250

minus150

minus50

50

150

250

Ang

ular

velo

city

(rad

s)

minus20

minus10

01020

Acce

lera

tion

(ms

2)

Open1 Close2 Raise3 Eat4 Drop5

1 2 3 4 5

Figure 8 Time series of sensing data in eating activity

in office increases Keeping incorrect postures during longworking time increases the risk of obesity of office workersTherefore it is necessary tomonitor the daily activity of officeworkers for correcting the bad habits

Motivated by this we have developed a sensorized beltcalled Waiston Belt [8] which aims to monitor a userrsquosdaily activities for maintaining health As products of belt-type wearable devices Welt [19] and Belty [20] are on saleThese provide the function of waist measurement activity

tracking and step counting However API for obtainingsensing data is not provided and it is not suitable for researchpurposes Generally it is hard for ordinary researchers toembed sensors battery BLE and vibrator into a small buckle-size case that can be worn in everyday life Nam et al [21]have proposed a belt-attached device for monitoring postureHowever the device has not reached a sufficient level to beused on a daily basis because of exposed wiring and uncoolappearance However by extending SenStick we succeeded

Journal of Sensors 11

C1

C2 C3

C4

Typical signal of each food

C1 rice amp dish C2 miso soup C3 noodles C4 shrimp

400

200

0

minus200

minus400

Figure 9 Eating activity recognition by sensorized chopsticks

SenStick

Waiston Belt

Figure 10 Waiston Belt based on SenStick

Table 5 Experimental result (food type)

Food type Number ofpieces of data Precision Recall 119865-Measure

C1 rice and dish 3011 0894 0945 0919C2 miso soup 2187 0907 0841 0873C3 noodles 2761 088 0896 0888C4 shrimp 2091 0907 088 0893

0896 0896 0895

to develop a stylish prototype of Waiston Belt shown inFigure 10 quickly Since the battery life of SenStick is 15 hourswith a small battery (60mAh) we can sense continuously inour daily lives As shown in Figure 11 it is possible to estimatevarious usersrsquo contexts (How much did you walk Are youusing the stairs Is posture good at work) by analyzing timeseries of sensing data obtained from the sensorized belt

An important point in this use case is that usersrsquo contextscan be estimated by combining multiple sensing data For

Table 6 Confusion matrix of food type recognition

PredictedC1 C2 C3 C4

ActualC1 rice and dish 094 004 001 000C2 miso soup 012 084 004 000C3 noodles 002 002 090 006C4 shrimp 001 001 011 088

example climbing stairs is estimated from the changes ofacceleration and atmospheric pressure The change of thehumidity data will be helpful to detect that a user enteredan air-conditioned room A SenStick makes it possible toeasily carry out research on behavior recognition with acombination of such multiple sensors

We introduce basic experiments about the posture recog-nition during deskwork by using this beltThe purpose of thisexperiment is to identify the posture type from the attitude

12 Journal of Sensors

Enter the lab

HumidityTemp

Humidity dropping

Pressure dropping

Working

Walking WalkingClimbing stairs Sitting (good posture) Sitting (bad posture)

AccsXAccsYAccsZ

60

40

20

1003

1002

1001

1000

999

25

2

15

1

05

0

minus15

minus1

minus05

Air pressure

Figure 11 Time series of sensing data in daily activity

C1 good posture C2 bad posture 1

C3 bad posture 2 C4 bad posture 3

Figure 12 Time series of sensing data in posture monitoring

angle of the belt calculated from 3 axial acceleration sensorsWe set the sensing rate as 10Hz and collected 6000 samples(10min) for each posture We assumed 4 classes (C1 goodposture C2 bad posture 1 C3 bad posture 2 and C4 badposture 3) as shown in Figure 12

In this experiment we calculated the recognition accu-racy of posture type classification by using Random Forestclassifier of Weka Tables 7 and 8 show the classificationresults The average recognition accuracy was 997 Inthe future based on the result of posture estimation we

Journal of Sensors 13

Sensorized glasses

Figure 13 Sensorized glasses

Table 7 Experimental result (posture type)

Posture type Number ofpieces of data Precision Recall 119865-Measure

C1 good posture 6000 1000 1000 1000C2 bad posture 1 6000 0994 0994 0994C3 bad posture 2 6000 0994 0994 0994C4 bad posture 3 6000 0999 0999 0999

0997 0997 0997

will develop a software application that suggests correctinghisher bad posture to a right posture during desk work

43 Indoor Localization by Sensorized Glasses We have pro-posed an indoor localization system using illuminance-basedtrilateration method [22] In this system we assume that theperson wears glasses having an illuminance sensor as shownin Figure 13 and controllable light sources are deployed in thetarget environment Our system controls lights andmeasuresthe variation of illuminance at the glasses As shown inFigure 14 the distances (1198891ndash1198893) from each light (1198971ndash1198973) to theuser (119906) are estimated from the distance-illumination modelconstructed in advance and userrsquos position is estimated basedon trilateration method using estimated distances

As a result of an experiment where three controllablelight sources were deployed in a room we confirmed thatour system could estimate the position within 05m erroron average By using SenStick we could easily develop thesensorized glasses equipped with illuminance sensor

44 Sensorized Airsoft Gun We have developed the sen-sorized airsoft gun for activity recognition in airsoft gameas shown in Figure 15 Airsoft game is a sport in whichparticipants eliminate opponents by hitting each other usingreplica weapons called airsoft guns The graph of a typicalsignal from the sensorized airsoft gun is shown in Figure 16A user can count the number of shots of a ball by attachingSenStick to his airsoft gun

45 Another Example Studentrsquos Unique Achievements andSurvey Results SenStick is also used in a lecture of IoT

Table 8 Confusion matrix of posture type recognition

PredictedC1 C2 C3 C4

ActualC1 good posture 1000 0000 0000 0000C2 bad posture 1 0000 0994 0006 0001C3 bad posture 2 0000 0006 0994 0000C4 bad posture 3 0000 0001 0000 0999

prototyping Students realize their unique IoT devices easilyby using SenSticks Here we introduce some of the developedIoT devices and survey results about the usefulness ofSenStick answered by students

Figure 17 shows a sensorized pencil with two SenSticksThe sensorized pencil is developed to recognize the charac-ters written by this pencil Even though two SenSticks areembedded to detect precise movement of pencil the size ofthe pencil is reasonable

Figure 18 shows a sensorized name tag anddigital signageSince we changed the battery from 60mAh to 900mAh thetag keeps running more than one week It aims to recordemployeesrsquo or studentsrsquo daily activities to support their healthThe digital signage informs the user who wears a sensorizedname tag about the userrsquos activity data when the user standsin front of it

Figure 19 shows a sensorized spoon used in the digitalfish catching game for children In this game the sensorizedspoon detects the scooping movement of the hand Thuschildren can catch the digital fish displayed by the projectionmapping by using sensorized spoonThis systemwas actuallyused at the universityrsquos open campus event and around100 children enjoyed this game and showed interest in IoTtechnology

The survey results obtained from the ten graduate stu-dents (ninemales and one female) who developed prototypesas mentioned above are shown in Table 9 A questionnaireis a five-grade form (5 strongly agree 4 agree a little 3neither agree nor disagree 2 disagree a little and 1 stronglydisagree) From the results of questions 1 and 2 manyparticipants think that SenStick platform is useful for IoT

14 Journal of Sensors

Position

Distance illumination model

Using trilateration method

a

Light 3

Light 2

Light 1

L = minus421d + 1496

L = minus063d + 501

05 1 15 2 25 3 35 4 450Distance between the illuminance sensor and the lighting device (m)

02468

1012141618

Illum

inan

ce v

alue

(lux

)

Distanceu

d1

d2

d3

l1 p1 x1

l3 p3 x3

l2 p2 x2

Figure 14 Indoor localization by sensorized glasses

Sensorized airsoft gun

Figure 15 Sensorized airsoft gun

research and IoT education Also the results of question 3show that many students are interested in the SenStick andwant to use it again From the results of questions 4 and 5we can see that usability and specification of SenStick arerelatively high There were opinions about the usefulness ofSenStick platform such as ldquoI could quickly try out ideas of IoTprototypingrdquo ldquoIt was easy to collect sensing datardquo and ldquoSinceSenStick is small it was easily attached tomost of the thingsrdquoOn the other hand there were opinions about improvements

such as ldquoI want to use higher sensing frequency (50ndash100Hz)rdquoldquoI want to process a sensing data on SenStickrdquo and ldquoI want tomodify the firmware itself depending on the application ofthe IoT devicerdquo

5 Conclusion

In this paper we have introduced a smart and comprehensivesensing platform called SenStick which is composed of

Journal of Sensors 15

Typical signal of airso gun

Hold a gun Ready

Shoot

1 2 3 4 5 6 70

00

30

60

91

21

51

82

12

42

73

03

33

63

94

24

54

85

15

45

76

06

36

66

97

27

57

88

18

48

79

09

39

69

910

210

510

811

111

411

712

012

312

612

913

2

minus600

minus400

minus200

0

200

400

600

800

acc_xacc_yacc_z

gyro_xgyro_y

acc_synthesis gyro_zgyro_synthesis

Figure 16 Time series of sensing data in shooting an airsoft gun

Sensorized pencil

Figure 17 Sensorized pencil

Sensorized name tagVisualization of sensor data

Figure 18 Sensorized name tag

Sensorized spoon Digital fishing game

Figure 19 Sensorized spoon

Table 9 Questionnaire results about SenStick

Question contents ScoreQ1 Do you think that SenStick will be useful for IoTresearch 49

Q2 Do you think that SenStick will be useful for IoTeducation 47

Q3 Do you want to use SenStick again 49Q4 Do you think that SenStick was easy to use 41Q5 Are you satisfied with specification of SenStick 41

hardware software and 3D case data The main purpose ofour platform is to simplify the sensorization of our personalbelongings

The hardware of SenStick is maybe one of the worldrsquossmallest sensor boards which includes 8 kinds of sensorsand BLE In addition to the hardware we also developedsupport software for managing monitoring recording andviewing the hardware By providing 3D case data all theresearchers can measure every tiny activity in daily life easilyand efficiently For an educational use it is also useful forunderstanding sensors and prototyping the desired systemincluding tiny censored things

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

This work is supported in part by JST PRESTO and JSPSKAKENHI (Grant no 17J10021) and ldquoResearch and Devel-opment of Innovative Network Technologies to Create theFuturerdquo the Commissioned Research of National Instituteof Information and Communications Technology (NICT)Japan

16 Journal of Sensors

References

[1] O Brdiczka M Langet J Maisonnasse and J L CrowleyldquoDetecting human behavior models from multimodal observa-tion in a smart homerdquo IEEE Transactions on Automation Scienceand Engineering vol 6 no 4 pp 588ndash597 2009

[2] J Chen A Kam J Zhang N Liu and L Shue ldquoBathroomactivity monitoring based on soundrdquo in Pervasive ComputingThird International Conference PERVASIVE 2005 MunichGermanyMay 8-13 2005 Proceedings vol 3468 ofLectureNotesin Computer Science pp 47ndash61 Springer Berlin Germany2002

[3] G Bauer K Stockinger and P Lukowicz ldquoRecognizing theUse-Mode of Kitchen Appliances from Their Current Consump-tionrdquo in Smart Sensing and Context vol 5741 of Lecture Notesin Computer Science pp 163ndash176 Springer Berlin HeidelbergBerlin Heidelberg 2009

[4] K Ouchi and M Doi ldquoIndoor-outdoor activity recognition bya smartphonerdquo in Proceedings of the the 2012 ACM Conferencep 537 Pittsburgh Pennsylvania September 2012

[5] A Anjum and M U Ilyas ldquoActivity recognition using smart-phone sensorsrdquo in Proceedings of the IEEE 10th ConsumerCommunications and Networking Conference (CCNC rsquo13) pp914ndash919 IEEE January 2013

[6] O D Lara and M A Labrador ldquoA survey on human activityrecognition using wearable sensorsrdquo IEEE CommunicationsSurveys amp Tutorials vol 15 no 3 pp 1192ndash1209 2013

[7] ldquoJins memerdquo httpsjins-memecom[8] Y Matsuda T Ishioka T Hasegawa et al ldquoWaistonBelt A belt

for monitoring your real abdominal circumference foreverrdquo inProceedings of the ACM International Joint Conference on Perva-sive and Ubiquitous Computing and the 2015 ACM InternationalSymposium on Wearable Computers UbiComp and ISWC 2015pp 329ndash332 September 2015

[9] M Kurahashi K Murao T Terada and M Tsukamoto ldquoPer-sonal identification system based on rotation of toilet paperrollsrdquo in Proceedings of the IEEE International Conference onPervasive Computing and CommunicationsWorkshops (PerComWorkshops rsquo17) pp 521ndash526 IEEE Kona Hawaii USA March2017

[10] ldquoHapiforkrdquo httpswwwhapicom[11] Y Arakawa ldquoSenStick Sensorize every thingsrdquo in Proceedings of

the ACM International Joint Conference on Pervasive and Ubiq-uitous Computing and the 2015 ACM International SymposiumonWearable Computers UbiComp and ISWC 2015 pp 349ndash352September 2015

[12] Y Nakamura T Kanehira Y Arakawa and K YasumotoldquoSenStick 2 Ultra tiny all-in-one sensor with wireless chargingrdquoinProceedings of the 2016ACM International Joint Conference onPervasive and Ubiquitous Computing UbiComp 2016 pp 337ndash340 September 2016

[13] ldquoSensortag cc2541 (texas instruments)rdquo httpwwwticomtoolcc2541dk-sensor

[14] ldquoIot smart modulerdquo httpwwwalpscomjiotsmart[15] ldquoTsnd121 (atr-promotions)rdquo httpwwwatr-pcomproducts

TSND121html[16] ldquoWax9 (axivity)rdquo httpaxivitycomproductwax9[17] Uk biobank ldquoUk biobankrdquo httpwwwukbiobankacuk[18] J C Seidell and J Halberstadt ldquoThe global burden of obesity

and the challenges of preventionrdquo Annals of Nutrition andMetabolism vol 66 supplement 2 pp 7ndash12 2015

[19] ldquoWeltThe smart belt for fashion and healthrdquo httpswwwwelt-corpcom

[20] ldquoBelty power The belt your really deserverdquo httpswwwbeltyco

[21] H Nam J-H Kim and J-I Kim ldquoSmart Belt A wearabledevice for managing abdominal obesityrdquo in Proceedings of theInternational Conference on Big Data and Smart ComputingBigComp 2016 pp 430ndash434 January 2016

[22] K Moriya M Fujimoto Y Arakawa and K Yasumoto ldquoIndoorlocalization based on distance-illuminance model and activecontrol of lighting devicesrdquo in Proceedings of the 2016 Interna-tional Conference on Indoor Positioning and Indoor NavigationIPIN 2016 pp 1ndash6 October 2016

RoboticsJournal of

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Volume 201

Submit your manuscripts athttpswwwhindawicom

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DistributedSensor Networks

International Journal of

Page 4: SenStick: Comprehensive Sensing Platform with an Ultra ...downloads.hindawi.com/journals/js/2017/6308302.pdf · ResearchArticle SenStick: Comprehensive Sensing Platform with an Ultra

4 Journal of Sensors

Table1Com

paris

onof

competitives

ensors

SenS

tick

3Se

nStic

k 2

Sens

orTa

g (C

C265

0)Io

T Sm

art M

odul

eTS

ND

121

WA

X9

Size

(mm)

50(119882

)times10

(119867)times

10(119863

)50

(119882)times

10(119867

)times10

(119863)

42(119882

)times32

(119867)times

10(119863

)44

(119882)times

27(119867

)times11(119863

)37

(119882)times

46(119867

)times12

(119863)23

(119882)times

325(119867

)times76

(119863)

Weight(g)

35

28mdash

227

Stand-alon

erecording

Itimes

timestimes

ISm

artpho

neapp

II

Itimes

timesDesktop

app

I998779

timesI

IBa

ttery

life

15(hou

r)1(year)

1(year)

6(hou

r)6(hou

r)Ba

ttery

type

Rechargeablerem

ovable

Rechargeable

Coinbatte

ryCoinbatte

ryRe

chargeable

Rechargeable

Network

BLE

BLE

BLE

Bluetooth20

BLE

Datap

rocessing

Itimes

timestimes

timestimes

I2CEx

tension

Itimes

timestimes

timestimes

Sensors

Acceleratio

nI

II

II

Gyro

II

timesI

IMagnetic

II

II

IAirpressure

II

II

ITemperature

II

Itimes

IHum

idity

II

Itimes

timesLight

II

Itimes

timesUV

Itimes

Itimes

timesMi c

timesI

timestimes

times

Journal of Sensors 5

Flash memory(back side)

50GG

60GB

Temperature amphumidity(SHT-20)

UV(VEML6070)

Pressure(LPS25HB)

Light(BH1780GLI)

Charger(BQ24090)

Micro USB

Li-Po battery

USBUART(CP2104)

9-axis(MPU-9250)

BLE(nRF51822)

Figure 2 Hardware of SenStick 2 (8 sensors BLE flash memory etc)

Table 2 Sensors embedded in SenStick 23

Type of sensor Model number Power consumption9-Axis MPU-9250 280 120583Asim37mAPressure LPS25HBTR 4 120583Asim25 120583ATemperaturehumidity SHT20 270 120583Asim330 120583AIlluminance BH1780GLI-E2 120120583Asim200 120583AUltraviolet (UV) VEML6070 100 120583Asim250 120583A

times 5mm (119863) and its weight is around 3 (g) including thebattery On this tiny board eight sensors (acceleration gyromagnetic light UV temperature humidity and pressure)flash memory (32Mbytes) BLE battery and charging circuitare densely embedded as shown in Figure 2 The modelnumber and power consumption of the sensors embedded inSenStick are listed in Table 2 All the sensors are popular chipsused worldwide and their performances are ensured by eachmanufacturer

The novelty of our hardware is the design of the sensorboard composed of the elaborated combination to satisfythe requirements presented in Section 2 This contributionbreaks down into the following aspects

(1) Combination of BLE and Flash Memory Almost allthe BLE-based devices equipped no large flash memoryto record sensing data because these devices are designedon the premise that sensing data are always recorded onsmartphones connected via BLE However the transmissionspeed of BLE is not enough for sending all the sensing dataof multiple sensors with the high sampling frequency As aresult some data will be dropped in the transmission Sincethe design of existing BLE-based devices is not appropriatefor research purpose we designed a new BLE-based sensingboard with a large flash memory which allows users torecord all the sensing data precisely Also it means that it canwork without holding a smartphone This ability is useful forsensing themotion of animals that cannot have a smartphone

(2) Selection of Powerful and Energy-Saving SoC Weselected Nordicrsquos nRF52832 SoC as SenStickrsquos microcon-troller Because the nRF52832 SoC is built around a 32-bit ARM Cortex-M4F CPU with 512 kB + 64 kB RAM itis extremely powerful Moreover the embedded 24GHztransceiver supports Bluetooth low energy and has extremelylow power consumption and it is the worldrsquos smallest classmodule Each sensor is connected via two I2C buses inde-pendently processing high-speed sensor (acceleration gyroandmagnetic) and low-speed sensor (light UV temperaturehumidity and pressure) taking into account the differencein bus occupancy time This makes it possible to realize highsampling with the motion sensors

(3) Application-Friendly Battery Type and Shape Since Sen-Stick is designed to bemounted on objects such as chopstickstoothbrushes and glasses it is desirable that the shape is thinand compact However sensors such as SensorTag equippedwith readily available coin batteries are inappropriate inshape because the footprint is square Also in the researchnonrechargeable coin batteries are costly and troublesomeFor those reasons we selected a thin and compact lithiumpolymer battery on SenStick This type of battery was harderto obtain than expected Therefore the procurement ofbattery has become the bottleneck of development

SenStickrsquos firmware is newly designed and developed byconsidering aforementioned requirements Generic Attribute(GATT) profile of SenStick provided by firmware is shownin Figure 3 The GATT server in SenStick provides threeservices (SenStick Control Service Metadata Read Serviceand Sensor Service) The SenStick Control Service providesoperation instructions such as starting and stopping sensingand logging The metadata reading service provides thefunction of reading metainformation of log data The SensorService provides functions to specify the sensing and loggingoperation of each sensor (0 acceleration 1 gyro 2 magnetic3 light 4 UV 5 temperaturehumidity 6 pressure) Also itprovides real-time sensing data reading function and log datareading function

6 Journal of Sensors

(32-)

Peripheral

GATT server

SenStick Control Service

Metadata Read Service

Read-write

Data notication

Flashmemory

(0 acceleration 1 gyro 2 magnetic 3 light

4 UV 5 temperaturehumidity 6 pressure)

GATT client

Central

~Sensor Service (0 6)

Figure 3 Generic Attribute (GATT) profile of SenStick

Table 3 BLE communication specification

Type of content SpecAdvertising packet Complete UUID flagScan response Local name (SenStick)Advertising packet interval 100ms (30 s)rarr1000ms (150 s)rarrsleepConnection interval max 80msmin 20msSupervision timeout 4000msSlave latency 0

Basically all the sensing data will be notified to a smart-phone in real time up to a transmission capacity of BLE Ifthe amount of sensing data exceeds the transmission capacitya part of sensing data will be dropped Therefore the real-time sensing data reading function is used formonitoring thestatus or for an interactive application that does not requireprecise data For precise data acquisition the log data readingfunction which guarantees the data reading order is usedThe communication specifications of BLE are described inTable 3

As shown in Table 4 the fixed capacities of a flashmemory are allocated to each sensorThe number of samplesindicates the total number of samples that can be recorded bythe allocated capacities

SenStick stops logging when the number of samples ofone sensor exceeds the storage capacity allocated for it Themaximum recording time of acceleration gyroscope andmagnetic sensor is 4 hours 40 minutes at sampling intervalof 10 milliseconds (sampling frequency 100Hz) and 14 hours10 minutes at sampling period of 30 milliseconds (samplingfrequency 33Hz) The maximum recording time of othersensors is 9 hours 26 minutes at sampling period of 200milliseconds (sampling frequency 5Hz)

32 Software We provide mobile applications for iOS andAndroid and a library for nodejs platform Common func-tions of them are operating function parameter settingfunction and real-time data view function An additional

Table 4 Allocated storage capacity per sensor

Amount of allocatedstorage capacity Samples

Acceleration 102Mbytes 17M samplesGyro 102Mbytes 17M samplesMagnetic 102Mbytes 17M samplesIlluminance 340KB 170K samplesUV (UV) 340KB 170K samplesHumidity 170KB 170K samplesAir temperature 170KB 170K samples

function of iOS application is a firmware update functionThose of Android application and nodejs applications areground truth video recording function and an ability to con-nect multiple SenSticks respectively Since all these softwareapplications are shared on GitHub as an own source softwareall the users can create their application based on their ownpurposes

Communication Protocol Here we explain the communica-tion protocol between SenStick and softwareThe explanationis based on ldquonode-SenStickrdquo which is our developed libraryfor nodejs Figure 4 shows a protocol sequence betweenSenStick and the central terminal (node-SenStick on Mac)

(I) Discovery First of all the application needs to find thesurrounding SenStick by using SenStickdiscovery() methodin our library When SenStickdiscovery() method finds aSenStick a callback function with an instance of SenStickobject containing the SenStickrsquos UUID and LocalName asarguments is called

(II) Connect If the target SenStick is found by the discoveryprocess the application is going to establish a connec-tion with it by calling SenStickprototypeconnectAndSetUp()method After establishing the connection this methodacquires the information necessary for operation from thetarget SenStick

Journal of Sensors 7

Central

Node-SenStick SenStick

SenStickdiscovery()

SenStickprototypeconnectAndSetUp()

Establish connection

SenStickprototypewriteSensorMeasurementCong()

Reect setting

SenStickprototypestartSensingAndLogging()

SenStickprototypenotifySensor()Start sensing

Select sensor to notify

Sensing data

SenStickprototypestopSensingAndLogging()

Stop sensing

Enable log notication

Select log data to notify

SenStickprototypewriteSensorLogReadoutTargetID()

Logging data

SenStickprototypedisconnect()

Disconnect

SenStickprototypenotifySensorLogData()

Periperal

(I) Discovery

(II) Connect

(III) Setting

(V) Obtaining log

(VI) Disconnect

(IV) Start sensingamp

RT receiving

Figure 4 Communication sequence between SenStick and node-SenStick

(III) Setting After establishing the connection the appli-cation sets three parameters (operation mode samplinginterval and measurement range) for each sensor by usingSenStickprototypewriteSensorMeasurementConfig()methodThe operation mode is a parameter that sets valida-tioninvalidation of sensing and logging of each sensor Thesampling interval and measurement range are parameters ofeach sensorwhichwill be changed by the sensing purpose andapplications

(IV) Start Sensing and Real-Time Data Receiving When Sen-StickprototypestartSensingAndLogging() method is calledSenStick starts sensing and logging Only valid sensorsrsquodata are transmitted via BLE and also recorded into aflash memory ID of each log is automatically assignedDuring sensing SenStick notifies sensing data in realtime To receive this notification the application callsSenStickprototypenotifySensor() method (Sensor representseach sensor name) and activates the notification for

8 Journal of Sensors

(i) Frequency(ii) Range

Device select Main view Settings Firmware update

Real-time view Log list in SenStick Download from SenStick

Selected sensorSensor name

Sensing frequency

Range of y-axis

Figure 5 iOS application for SenStick

the sensor When a notification of sensing data isreceived sensorChange event will be issued If Sen-StickprototypestopSensingAndLogging() method is calledSenStick stops sensing and logging

(V) Log Data Reading To read the log data recordedin the flash memory to the application the applicationcalls SenStickprototypenotifySensorLogData()method (Sen-sor represents each sensor name) and enables log datanotification If the application specifies a valid log ID bycalling SenStickprototypewriteSensorLogReadoutTargetID()method SenStick starts informing the sensing data recordedin the log sequentially To receive notification of log datasensorLogDataReceived event will be issued By receiving thisnotification by the application it is possible to acquire the logdata recorded in the memory

(VI) Disconnect After finishing the data acquisition Sen-Stickprototypedisconnect() method will be called to releasethe connection of SenStick And SenStick will begin advertis-ing again

User InterfaceWe explain the user interface of the applicationbased on iOS version Figure 5 shows a sophisticated mobileapplication for SenStick As shown in Figure 5 the userselects target SenStick from devices listThen the applicationshows the main view In the main view the user can selectsensors to be acquired For immediate data monitoring allthe received data via BLE can be shown in the graph view inreal time The application can control all the settings of eachsensor such as sample frequency and sensing range And alsoit has firmware update function Sensing data stored in theflash memory of SenStick is viewed from log list and it can be

downloaded to the smartphone in CSV formatThis functionprevents data loss caused by BLE communication

33 3D Case Data In order to make sensorized things anattachment case for mounting the SenStick is required Wedesigned the basic 3D case of SenStick By extending thisbasic case users can design derivative cases for chopsticksglasses and so on Actually our laboratory has succeeded indesigning cases for several things as shown in Figure 6 Allof these cases data are shared on our community site Eachcase is designed in a few minutes thanks to the basic caseFor example SenStick case for glasses is made by combiningbasic case of SenStick and open 3D data of glasses distributedby i Design Studio In addition due to the popularizationof 3D printers in the world and enhancement of tools andreferences for 3D modeling all the users can print out theirdesired SenStick cases by themselves and attach our sensor toeverything

4 Case Studies of SenStick

SenStick an ultra small sensor can be embedded into varioustiny things in our daily life Our first target is our dailybelongings such as glasses chopsticks and pens becausesensorization of daily belongings is good to intuitively under-standwhat can be sensed andwhich sensor is usefuleffectiveSenStick can upgrade normal glasses to intelligent oneswhichcan monitor a head movement posture and the amount ofsunlight received If SenStick is embedded into chopsticksand pens we can observe eating activities and handwritingactivities respectively In the future we will embed it into astick for elderlymonitoring sports gearsmusical instrument

Journal of Sensors 9

Chopsticks Tooth brush Glasses Umbrella Name card Cookware

A user can design original case data in a few minutes

Basic 3D case of SenStick

Figure 6 3D case data of SenStick

Sensorized

Chopsticks

Figure 7 Eating activity recognition by sensorized chopsticks

kitchen utensils animals stuffed toy a remote control andany kind of things that can be sensing targets In this sectionwe introduce some case studies results utilizing SenStick

41 Eating Behavior Recognition by Sensorized Chopsticks Inmany cases East Asian people eat meals with chopsticksBy analyzing the data obtained from chopsticks there is apossibility that we can estimate the eating habits includinganswers for questions like ldquohow did you eatrdquo and ldquowhatdid you eatrdquo and so forth These data are very useful formaking healthcare applications and leading a healthy lifeThis is one reason why we develop the sensorized chopsticksusing SenStick shown in Figure 7

Research goals of the eating behavior recognition by sen-sorized chopsticks are as follows G1 (behavior) recognizingthe timing and speed and detecting ldquocroprdquo and ldquointakerdquoaction G2 (food type) classifying food content like ricenoodles and so forth from the eating behavior and G3(person) identifying an individual from using chopsticks Inthis paper we show the research results on G1 and G2

We conducted basic experiments to recognize the eatingbehavior (G1 and G2) We used acceleration and gyroscopedata measured by SenStick where the sensing rate is 10HzThe purpose of experiment 1 is to understand the basic

characteristics of sensing data from sensorized chopstickwhen a participant ate chips The purpose of experiment 2 isto identify the food type from the difference in movement ofchopsticks We assumed 4 classes (C1 rice and dish C2 misosoup C3 noodles and C4 shrimp) as food types

Figure 8 shows time series of sensing data for a set ofeating actions in experiment 1 The eating actions are cropaction and intake action In crop action 6-axis motionsensors detect movement (open and close) of the chopsticksIn intake action sensors detect movement of the arm fromthe dish to the mouth Each graph in Figure 8 shows certaincorrelation with both actions

Figure 9 shows a typical signal for each food in exper-iment 2 In this experiment we calculate the recognitionaccuracy of food type classification by using Random Forestclassier ofWeka Tables 5 and 6 show the classification resultsThe average recognition accuracy was 896 If we did nothave a SenStick it would be hard to develop the sensorizedchopsticks that can be used in the actual environment

42 Daily Activity Monitoring by a Sensorized Belt Recentlycorpulent people are rapidly increasing and about 700 mil-lion people belong to the obesity group [18] Nowadays manyoffice workers use computers heavily and their sitting time

10 Journal of Sensors

Step 1 crop action Step 2 intake action

Lower

Upper

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

X

Y

Z

Synthetic

X

Y

Z

Synthetic

X

Y

Z

Synthetic

X

Y

Z

Synthetic

minus250

minus150

minus50

50

150

250

Ang

ular

velo

city

(rad

s)

minus20

minus10

01020

Acce

lera

tion

(ms

2)

minus250

minus150

minus50

50

150

250

Ang

ular

velo

city

(rad

s)

minus20

minus10

01020

Acce

lera

tion

(ms

2)

Open1 Close2 Raise3 Eat4 Drop5

1 2 3 4 5

Figure 8 Time series of sensing data in eating activity

in office increases Keeping incorrect postures during longworking time increases the risk of obesity of office workersTherefore it is necessary tomonitor the daily activity of officeworkers for correcting the bad habits

Motivated by this we have developed a sensorized beltcalled Waiston Belt [8] which aims to monitor a userrsquosdaily activities for maintaining health As products of belt-type wearable devices Welt [19] and Belty [20] are on saleThese provide the function of waist measurement activity

tracking and step counting However API for obtainingsensing data is not provided and it is not suitable for researchpurposes Generally it is hard for ordinary researchers toembed sensors battery BLE and vibrator into a small buckle-size case that can be worn in everyday life Nam et al [21]have proposed a belt-attached device for monitoring postureHowever the device has not reached a sufficient level to beused on a daily basis because of exposed wiring and uncoolappearance However by extending SenStick we succeeded

Journal of Sensors 11

C1

C2 C3

C4

Typical signal of each food

C1 rice amp dish C2 miso soup C3 noodles C4 shrimp

400

200

0

minus200

minus400

Figure 9 Eating activity recognition by sensorized chopsticks

SenStick

Waiston Belt

Figure 10 Waiston Belt based on SenStick

Table 5 Experimental result (food type)

Food type Number ofpieces of data Precision Recall 119865-Measure

C1 rice and dish 3011 0894 0945 0919C2 miso soup 2187 0907 0841 0873C3 noodles 2761 088 0896 0888C4 shrimp 2091 0907 088 0893

0896 0896 0895

to develop a stylish prototype of Waiston Belt shown inFigure 10 quickly Since the battery life of SenStick is 15 hourswith a small battery (60mAh) we can sense continuously inour daily lives As shown in Figure 11 it is possible to estimatevarious usersrsquo contexts (How much did you walk Are youusing the stairs Is posture good at work) by analyzing timeseries of sensing data obtained from the sensorized belt

An important point in this use case is that usersrsquo contextscan be estimated by combining multiple sensing data For

Table 6 Confusion matrix of food type recognition

PredictedC1 C2 C3 C4

ActualC1 rice and dish 094 004 001 000C2 miso soup 012 084 004 000C3 noodles 002 002 090 006C4 shrimp 001 001 011 088

example climbing stairs is estimated from the changes ofacceleration and atmospheric pressure The change of thehumidity data will be helpful to detect that a user enteredan air-conditioned room A SenStick makes it possible toeasily carry out research on behavior recognition with acombination of such multiple sensors

We introduce basic experiments about the posture recog-nition during deskwork by using this beltThe purpose of thisexperiment is to identify the posture type from the attitude

12 Journal of Sensors

Enter the lab

HumidityTemp

Humidity dropping

Pressure dropping

Working

Walking WalkingClimbing stairs Sitting (good posture) Sitting (bad posture)

AccsXAccsYAccsZ

60

40

20

1003

1002

1001

1000

999

25

2

15

1

05

0

minus15

minus1

minus05

Air pressure

Figure 11 Time series of sensing data in daily activity

C1 good posture C2 bad posture 1

C3 bad posture 2 C4 bad posture 3

Figure 12 Time series of sensing data in posture monitoring

angle of the belt calculated from 3 axial acceleration sensorsWe set the sensing rate as 10Hz and collected 6000 samples(10min) for each posture We assumed 4 classes (C1 goodposture C2 bad posture 1 C3 bad posture 2 and C4 badposture 3) as shown in Figure 12

In this experiment we calculated the recognition accu-racy of posture type classification by using Random Forestclassifier of Weka Tables 7 and 8 show the classificationresults The average recognition accuracy was 997 Inthe future based on the result of posture estimation we

Journal of Sensors 13

Sensorized glasses

Figure 13 Sensorized glasses

Table 7 Experimental result (posture type)

Posture type Number ofpieces of data Precision Recall 119865-Measure

C1 good posture 6000 1000 1000 1000C2 bad posture 1 6000 0994 0994 0994C3 bad posture 2 6000 0994 0994 0994C4 bad posture 3 6000 0999 0999 0999

0997 0997 0997

will develop a software application that suggests correctinghisher bad posture to a right posture during desk work

43 Indoor Localization by Sensorized Glasses We have pro-posed an indoor localization system using illuminance-basedtrilateration method [22] In this system we assume that theperson wears glasses having an illuminance sensor as shownin Figure 13 and controllable light sources are deployed in thetarget environment Our system controls lights andmeasuresthe variation of illuminance at the glasses As shown inFigure 14 the distances (1198891ndash1198893) from each light (1198971ndash1198973) to theuser (119906) are estimated from the distance-illumination modelconstructed in advance and userrsquos position is estimated basedon trilateration method using estimated distances

As a result of an experiment where three controllablelight sources were deployed in a room we confirmed thatour system could estimate the position within 05m erroron average By using SenStick we could easily develop thesensorized glasses equipped with illuminance sensor

44 Sensorized Airsoft Gun We have developed the sen-sorized airsoft gun for activity recognition in airsoft gameas shown in Figure 15 Airsoft game is a sport in whichparticipants eliminate opponents by hitting each other usingreplica weapons called airsoft guns The graph of a typicalsignal from the sensorized airsoft gun is shown in Figure 16A user can count the number of shots of a ball by attachingSenStick to his airsoft gun

45 Another Example Studentrsquos Unique Achievements andSurvey Results SenStick is also used in a lecture of IoT

Table 8 Confusion matrix of posture type recognition

PredictedC1 C2 C3 C4

ActualC1 good posture 1000 0000 0000 0000C2 bad posture 1 0000 0994 0006 0001C3 bad posture 2 0000 0006 0994 0000C4 bad posture 3 0000 0001 0000 0999

prototyping Students realize their unique IoT devices easilyby using SenSticks Here we introduce some of the developedIoT devices and survey results about the usefulness ofSenStick answered by students

Figure 17 shows a sensorized pencil with two SenSticksThe sensorized pencil is developed to recognize the charac-ters written by this pencil Even though two SenSticks areembedded to detect precise movement of pencil the size ofthe pencil is reasonable

Figure 18 shows a sensorized name tag anddigital signageSince we changed the battery from 60mAh to 900mAh thetag keeps running more than one week It aims to recordemployeesrsquo or studentsrsquo daily activities to support their healthThe digital signage informs the user who wears a sensorizedname tag about the userrsquos activity data when the user standsin front of it

Figure 19 shows a sensorized spoon used in the digitalfish catching game for children In this game the sensorizedspoon detects the scooping movement of the hand Thuschildren can catch the digital fish displayed by the projectionmapping by using sensorized spoonThis systemwas actuallyused at the universityrsquos open campus event and around100 children enjoyed this game and showed interest in IoTtechnology

The survey results obtained from the ten graduate stu-dents (ninemales and one female) who developed prototypesas mentioned above are shown in Table 9 A questionnaireis a five-grade form (5 strongly agree 4 agree a little 3neither agree nor disagree 2 disagree a little and 1 stronglydisagree) From the results of questions 1 and 2 manyparticipants think that SenStick platform is useful for IoT

14 Journal of Sensors

Position

Distance illumination model

Using trilateration method

a

Light 3

Light 2

Light 1

L = minus421d + 1496

L = minus063d + 501

05 1 15 2 25 3 35 4 450Distance between the illuminance sensor and the lighting device (m)

02468

1012141618

Illum

inan

ce v

alue

(lux

)

Distanceu

d1

d2

d3

l1 p1 x1

l3 p3 x3

l2 p2 x2

Figure 14 Indoor localization by sensorized glasses

Sensorized airsoft gun

Figure 15 Sensorized airsoft gun

research and IoT education Also the results of question 3show that many students are interested in the SenStick andwant to use it again From the results of questions 4 and 5we can see that usability and specification of SenStick arerelatively high There were opinions about the usefulness ofSenStick platform such as ldquoI could quickly try out ideas of IoTprototypingrdquo ldquoIt was easy to collect sensing datardquo and ldquoSinceSenStick is small it was easily attached tomost of the thingsrdquoOn the other hand there were opinions about improvements

such as ldquoI want to use higher sensing frequency (50ndash100Hz)rdquoldquoI want to process a sensing data on SenStickrdquo and ldquoI want tomodify the firmware itself depending on the application ofthe IoT devicerdquo

5 Conclusion

In this paper we have introduced a smart and comprehensivesensing platform called SenStick which is composed of

Journal of Sensors 15

Typical signal of airso gun

Hold a gun Ready

Shoot

1 2 3 4 5 6 70

00

30

60

91

21

51

82

12

42

73

03

33

63

94

24

54

85

15

45

76

06

36

66

97

27

57

88

18

48

79

09

39

69

910

210

510

811

111

411

712

012

312

612

913

2

minus600

minus400

minus200

0

200

400

600

800

acc_xacc_yacc_z

gyro_xgyro_y

acc_synthesis gyro_zgyro_synthesis

Figure 16 Time series of sensing data in shooting an airsoft gun

Sensorized pencil

Figure 17 Sensorized pencil

Sensorized name tagVisualization of sensor data

Figure 18 Sensorized name tag

Sensorized spoon Digital fishing game

Figure 19 Sensorized spoon

Table 9 Questionnaire results about SenStick

Question contents ScoreQ1 Do you think that SenStick will be useful for IoTresearch 49

Q2 Do you think that SenStick will be useful for IoTeducation 47

Q3 Do you want to use SenStick again 49Q4 Do you think that SenStick was easy to use 41Q5 Are you satisfied with specification of SenStick 41

hardware software and 3D case data The main purpose ofour platform is to simplify the sensorization of our personalbelongings

The hardware of SenStick is maybe one of the worldrsquossmallest sensor boards which includes 8 kinds of sensorsand BLE In addition to the hardware we also developedsupport software for managing monitoring recording andviewing the hardware By providing 3D case data all theresearchers can measure every tiny activity in daily life easilyand efficiently For an educational use it is also useful forunderstanding sensors and prototyping the desired systemincluding tiny censored things

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

This work is supported in part by JST PRESTO and JSPSKAKENHI (Grant no 17J10021) and ldquoResearch and Devel-opment of Innovative Network Technologies to Create theFuturerdquo the Commissioned Research of National Instituteof Information and Communications Technology (NICT)Japan

16 Journal of Sensors

References

[1] O Brdiczka M Langet J Maisonnasse and J L CrowleyldquoDetecting human behavior models from multimodal observa-tion in a smart homerdquo IEEE Transactions on Automation Scienceand Engineering vol 6 no 4 pp 588ndash597 2009

[2] J Chen A Kam J Zhang N Liu and L Shue ldquoBathroomactivity monitoring based on soundrdquo in Pervasive ComputingThird International Conference PERVASIVE 2005 MunichGermanyMay 8-13 2005 Proceedings vol 3468 ofLectureNotesin Computer Science pp 47ndash61 Springer Berlin Germany2002

[3] G Bauer K Stockinger and P Lukowicz ldquoRecognizing theUse-Mode of Kitchen Appliances from Their Current Consump-tionrdquo in Smart Sensing and Context vol 5741 of Lecture Notesin Computer Science pp 163ndash176 Springer Berlin HeidelbergBerlin Heidelberg 2009

[4] K Ouchi and M Doi ldquoIndoor-outdoor activity recognition bya smartphonerdquo in Proceedings of the the 2012 ACM Conferencep 537 Pittsburgh Pennsylvania September 2012

[5] A Anjum and M U Ilyas ldquoActivity recognition using smart-phone sensorsrdquo in Proceedings of the IEEE 10th ConsumerCommunications and Networking Conference (CCNC rsquo13) pp914ndash919 IEEE January 2013

[6] O D Lara and M A Labrador ldquoA survey on human activityrecognition using wearable sensorsrdquo IEEE CommunicationsSurveys amp Tutorials vol 15 no 3 pp 1192ndash1209 2013

[7] ldquoJins memerdquo httpsjins-memecom[8] Y Matsuda T Ishioka T Hasegawa et al ldquoWaistonBelt A belt

for monitoring your real abdominal circumference foreverrdquo inProceedings of the ACM International Joint Conference on Perva-sive and Ubiquitous Computing and the 2015 ACM InternationalSymposium on Wearable Computers UbiComp and ISWC 2015pp 329ndash332 September 2015

[9] M Kurahashi K Murao T Terada and M Tsukamoto ldquoPer-sonal identification system based on rotation of toilet paperrollsrdquo in Proceedings of the IEEE International Conference onPervasive Computing and CommunicationsWorkshops (PerComWorkshops rsquo17) pp 521ndash526 IEEE Kona Hawaii USA March2017

[10] ldquoHapiforkrdquo httpswwwhapicom[11] Y Arakawa ldquoSenStick Sensorize every thingsrdquo in Proceedings of

the ACM International Joint Conference on Pervasive and Ubiq-uitous Computing and the 2015 ACM International SymposiumonWearable Computers UbiComp and ISWC 2015 pp 349ndash352September 2015

[12] Y Nakamura T Kanehira Y Arakawa and K YasumotoldquoSenStick 2 Ultra tiny all-in-one sensor with wireless chargingrdquoinProceedings of the 2016ACM International Joint Conference onPervasive and Ubiquitous Computing UbiComp 2016 pp 337ndash340 September 2016

[13] ldquoSensortag cc2541 (texas instruments)rdquo httpwwwticomtoolcc2541dk-sensor

[14] ldquoIot smart modulerdquo httpwwwalpscomjiotsmart[15] ldquoTsnd121 (atr-promotions)rdquo httpwwwatr-pcomproducts

TSND121html[16] ldquoWax9 (axivity)rdquo httpaxivitycomproductwax9[17] Uk biobank ldquoUk biobankrdquo httpwwwukbiobankacuk[18] J C Seidell and J Halberstadt ldquoThe global burden of obesity

and the challenges of preventionrdquo Annals of Nutrition andMetabolism vol 66 supplement 2 pp 7ndash12 2015

[19] ldquoWeltThe smart belt for fashion and healthrdquo httpswwwwelt-corpcom

[20] ldquoBelty power The belt your really deserverdquo httpswwwbeltyco

[21] H Nam J-H Kim and J-I Kim ldquoSmart Belt A wearabledevice for managing abdominal obesityrdquo in Proceedings of theInternational Conference on Big Data and Smart ComputingBigComp 2016 pp 430ndash434 January 2016

[22] K Moriya M Fujimoto Y Arakawa and K Yasumoto ldquoIndoorlocalization based on distance-illuminance model and activecontrol of lighting devicesrdquo in Proceedings of the 2016 Interna-tional Conference on Indoor Positioning and Indoor NavigationIPIN 2016 pp 1ndash6 October 2016

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal of

Volume 201

Submit your manuscripts athttpswwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

Page 5: SenStick: Comprehensive Sensing Platform with an Ultra ...downloads.hindawi.com/journals/js/2017/6308302.pdf · ResearchArticle SenStick: Comprehensive Sensing Platform with an Ultra

Journal of Sensors 5

Flash memory(back side)

50GG

60GB

Temperature amphumidity(SHT-20)

UV(VEML6070)

Pressure(LPS25HB)

Light(BH1780GLI)

Charger(BQ24090)

Micro USB

Li-Po battery

USBUART(CP2104)

9-axis(MPU-9250)

BLE(nRF51822)

Figure 2 Hardware of SenStick 2 (8 sensors BLE flash memory etc)

Table 2 Sensors embedded in SenStick 23

Type of sensor Model number Power consumption9-Axis MPU-9250 280 120583Asim37mAPressure LPS25HBTR 4 120583Asim25 120583ATemperaturehumidity SHT20 270 120583Asim330 120583AIlluminance BH1780GLI-E2 120120583Asim200 120583AUltraviolet (UV) VEML6070 100 120583Asim250 120583A

times 5mm (119863) and its weight is around 3 (g) including thebattery On this tiny board eight sensors (acceleration gyromagnetic light UV temperature humidity and pressure)flash memory (32Mbytes) BLE battery and charging circuitare densely embedded as shown in Figure 2 The modelnumber and power consumption of the sensors embedded inSenStick are listed in Table 2 All the sensors are popular chipsused worldwide and their performances are ensured by eachmanufacturer

The novelty of our hardware is the design of the sensorboard composed of the elaborated combination to satisfythe requirements presented in Section 2 This contributionbreaks down into the following aspects

(1) Combination of BLE and Flash Memory Almost allthe BLE-based devices equipped no large flash memoryto record sensing data because these devices are designedon the premise that sensing data are always recorded onsmartphones connected via BLE However the transmissionspeed of BLE is not enough for sending all the sensing dataof multiple sensors with the high sampling frequency As aresult some data will be dropped in the transmission Sincethe design of existing BLE-based devices is not appropriatefor research purpose we designed a new BLE-based sensingboard with a large flash memory which allows users torecord all the sensing data precisely Also it means that it canwork without holding a smartphone This ability is useful forsensing themotion of animals that cannot have a smartphone

(2) Selection of Powerful and Energy-Saving SoC Weselected Nordicrsquos nRF52832 SoC as SenStickrsquos microcon-troller Because the nRF52832 SoC is built around a 32-bit ARM Cortex-M4F CPU with 512 kB + 64 kB RAM itis extremely powerful Moreover the embedded 24GHztransceiver supports Bluetooth low energy and has extremelylow power consumption and it is the worldrsquos smallest classmodule Each sensor is connected via two I2C buses inde-pendently processing high-speed sensor (acceleration gyroandmagnetic) and low-speed sensor (light UV temperaturehumidity and pressure) taking into account the differencein bus occupancy time This makes it possible to realize highsampling with the motion sensors

(3) Application-Friendly Battery Type and Shape Since Sen-Stick is designed to bemounted on objects such as chopstickstoothbrushes and glasses it is desirable that the shape is thinand compact However sensors such as SensorTag equippedwith readily available coin batteries are inappropriate inshape because the footprint is square Also in the researchnonrechargeable coin batteries are costly and troublesomeFor those reasons we selected a thin and compact lithiumpolymer battery on SenStick This type of battery was harderto obtain than expected Therefore the procurement ofbattery has become the bottleneck of development

SenStickrsquos firmware is newly designed and developed byconsidering aforementioned requirements Generic Attribute(GATT) profile of SenStick provided by firmware is shownin Figure 3 The GATT server in SenStick provides threeservices (SenStick Control Service Metadata Read Serviceand Sensor Service) The SenStick Control Service providesoperation instructions such as starting and stopping sensingand logging The metadata reading service provides thefunction of reading metainformation of log data The SensorService provides functions to specify the sensing and loggingoperation of each sensor (0 acceleration 1 gyro 2 magnetic3 light 4 UV 5 temperaturehumidity 6 pressure) Also itprovides real-time sensing data reading function and log datareading function

6 Journal of Sensors

(32-)

Peripheral

GATT server

SenStick Control Service

Metadata Read Service

Read-write

Data notication

Flashmemory

(0 acceleration 1 gyro 2 magnetic 3 light

4 UV 5 temperaturehumidity 6 pressure)

GATT client

Central

~Sensor Service (0 6)

Figure 3 Generic Attribute (GATT) profile of SenStick

Table 3 BLE communication specification

Type of content SpecAdvertising packet Complete UUID flagScan response Local name (SenStick)Advertising packet interval 100ms (30 s)rarr1000ms (150 s)rarrsleepConnection interval max 80msmin 20msSupervision timeout 4000msSlave latency 0

Basically all the sensing data will be notified to a smart-phone in real time up to a transmission capacity of BLE Ifthe amount of sensing data exceeds the transmission capacitya part of sensing data will be dropped Therefore the real-time sensing data reading function is used formonitoring thestatus or for an interactive application that does not requireprecise data For precise data acquisition the log data readingfunction which guarantees the data reading order is usedThe communication specifications of BLE are described inTable 3

As shown in Table 4 the fixed capacities of a flashmemory are allocated to each sensorThe number of samplesindicates the total number of samples that can be recorded bythe allocated capacities

SenStick stops logging when the number of samples ofone sensor exceeds the storage capacity allocated for it Themaximum recording time of acceleration gyroscope andmagnetic sensor is 4 hours 40 minutes at sampling intervalof 10 milliseconds (sampling frequency 100Hz) and 14 hours10 minutes at sampling period of 30 milliseconds (samplingfrequency 33Hz) The maximum recording time of othersensors is 9 hours 26 minutes at sampling period of 200milliseconds (sampling frequency 5Hz)

32 Software We provide mobile applications for iOS andAndroid and a library for nodejs platform Common func-tions of them are operating function parameter settingfunction and real-time data view function An additional

Table 4 Allocated storage capacity per sensor

Amount of allocatedstorage capacity Samples

Acceleration 102Mbytes 17M samplesGyro 102Mbytes 17M samplesMagnetic 102Mbytes 17M samplesIlluminance 340KB 170K samplesUV (UV) 340KB 170K samplesHumidity 170KB 170K samplesAir temperature 170KB 170K samples

function of iOS application is a firmware update functionThose of Android application and nodejs applications areground truth video recording function and an ability to con-nect multiple SenSticks respectively Since all these softwareapplications are shared on GitHub as an own source softwareall the users can create their application based on their ownpurposes

Communication Protocol Here we explain the communica-tion protocol between SenStick and softwareThe explanationis based on ldquonode-SenStickrdquo which is our developed libraryfor nodejs Figure 4 shows a protocol sequence betweenSenStick and the central terminal (node-SenStick on Mac)

(I) Discovery First of all the application needs to find thesurrounding SenStick by using SenStickdiscovery() methodin our library When SenStickdiscovery() method finds aSenStick a callback function with an instance of SenStickobject containing the SenStickrsquos UUID and LocalName asarguments is called

(II) Connect If the target SenStick is found by the discoveryprocess the application is going to establish a connec-tion with it by calling SenStickprototypeconnectAndSetUp()method After establishing the connection this methodacquires the information necessary for operation from thetarget SenStick

Journal of Sensors 7

Central

Node-SenStick SenStick

SenStickdiscovery()

SenStickprototypeconnectAndSetUp()

Establish connection

SenStickprototypewriteSensorMeasurementCong()

Reect setting

SenStickprototypestartSensingAndLogging()

SenStickprototypenotifySensor()Start sensing

Select sensor to notify

Sensing data

SenStickprototypestopSensingAndLogging()

Stop sensing

Enable log notication

Select log data to notify

SenStickprototypewriteSensorLogReadoutTargetID()

Logging data

SenStickprototypedisconnect()

Disconnect

SenStickprototypenotifySensorLogData()

Periperal

(I) Discovery

(II) Connect

(III) Setting

(V) Obtaining log

(VI) Disconnect

(IV) Start sensingamp

RT receiving

Figure 4 Communication sequence between SenStick and node-SenStick

(III) Setting After establishing the connection the appli-cation sets three parameters (operation mode samplinginterval and measurement range) for each sensor by usingSenStickprototypewriteSensorMeasurementConfig()methodThe operation mode is a parameter that sets valida-tioninvalidation of sensing and logging of each sensor Thesampling interval and measurement range are parameters ofeach sensorwhichwill be changed by the sensing purpose andapplications

(IV) Start Sensing and Real-Time Data Receiving When Sen-StickprototypestartSensingAndLogging() method is calledSenStick starts sensing and logging Only valid sensorsrsquodata are transmitted via BLE and also recorded into aflash memory ID of each log is automatically assignedDuring sensing SenStick notifies sensing data in realtime To receive this notification the application callsSenStickprototypenotifySensor() method (Sensor representseach sensor name) and activates the notification for

8 Journal of Sensors

(i) Frequency(ii) Range

Device select Main view Settings Firmware update

Real-time view Log list in SenStick Download from SenStick

Selected sensorSensor name

Sensing frequency

Range of y-axis

Figure 5 iOS application for SenStick

the sensor When a notification of sensing data isreceived sensorChange event will be issued If Sen-StickprototypestopSensingAndLogging() method is calledSenStick stops sensing and logging

(V) Log Data Reading To read the log data recordedin the flash memory to the application the applicationcalls SenStickprototypenotifySensorLogData()method (Sen-sor represents each sensor name) and enables log datanotification If the application specifies a valid log ID bycalling SenStickprototypewriteSensorLogReadoutTargetID()method SenStick starts informing the sensing data recordedin the log sequentially To receive notification of log datasensorLogDataReceived event will be issued By receiving thisnotification by the application it is possible to acquire the logdata recorded in the memory

(VI) Disconnect After finishing the data acquisition Sen-Stickprototypedisconnect() method will be called to releasethe connection of SenStick And SenStick will begin advertis-ing again

User InterfaceWe explain the user interface of the applicationbased on iOS version Figure 5 shows a sophisticated mobileapplication for SenStick As shown in Figure 5 the userselects target SenStick from devices listThen the applicationshows the main view In the main view the user can selectsensors to be acquired For immediate data monitoring allthe received data via BLE can be shown in the graph view inreal time The application can control all the settings of eachsensor such as sample frequency and sensing range And alsoit has firmware update function Sensing data stored in theflash memory of SenStick is viewed from log list and it can be

downloaded to the smartphone in CSV formatThis functionprevents data loss caused by BLE communication

33 3D Case Data In order to make sensorized things anattachment case for mounting the SenStick is required Wedesigned the basic 3D case of SenStick By extending thisbasic case users can design derivative cases for chopsticksglasses and so on Actually our laboratory has succeeded indesigning cases for several things as shown in Figure 6 Allof these cases data are shared on our community site Eachcase is designed in a few minutes thanks to the basic caseFor example SenStick case for glasses is made by combiningbasic case of SenStick and open 3D data of glasses distributedby i Design Studio In addition due to the popularizationof 3D printers in the world and enhancement of tools andreferences for 3D modeling all the users can print out theirdesired SenStick cases by themselves and attach our sensor toeverything

4 Case Studies of SenStick

SenStick an ultra small sensor can be embedded into varioustiny things in our daily life Our first target is our dailybelongings such as glasses chopsticks and pens becausesensorization of daily belongings is good to intuitively under-standwhat can be sensed andwhich sensor is usefuleffectiveSenStick can upgrade normal glasses to intelligent oneswhichcan monitor a head movement posture and the amount ofsunlight received If SenStick is embedded into chopsticksand pens we can observe eating activities and handwritingactivities respectively In the future we will embed it into astick for elderlymonitoring sports gearsmusical instrument

Journal of Sensors 9

Chopsticks Tooth brush Glasses Umbrella Name card Cookware

A user can design original case data in a few minutes

Basic 3D case of SenStick

Figure 6 3D case data of SenStick

Sensorized

Chopsticks

Figure 7 Eating activity recognition by sensorized chopsticks

kitchen utensils animals stuffed toy a remote control andany kind of things that can be sensing targets In this sectionwe introduce some case studies results utilizing SenStick

41 Eating Behavior Recognition by Sensorized Chopsticks Inmany cases East Asian people eat meals with chopsticksBy analyzing the data obtained from chopsticks there is apossibility that we can estimate the eating habits includinganswers for questions like ldquohow did you eatrdquo and ldquowhatdid you eatrdquo and so forth These data are very useful formaking healthcare applications and leading a healthy lifeThis is one reason why we develop the sensorized chopsticksusing SenStick shown in Figure 7

Research goals of the eating behavior recognition by sen-sorized chopsticks are as follows G1 (behavior) recognizingthe timing and speed and detecting ldquocroprdquo and ldquointakerdquoaction G2 (food type) classifying food content like ricenoodles and so forth from the eating behavior and G3(person) identifying an individual from using chopsticks Inthis paper we show the research results on G1 and G2

We conducted basic experiments to recognize the eatingbehavior (G1 and G2) We used acceleration and gyroscopedata measured by SenStick where the sensing rate is 10HzThe purpose of experiment 1 is to understand the basic

characteristics of sensing data from sensorized chopstickwhen a participant ate chips The purpose of experiment 2 isto identify the food type from the difference in movement ofchopsticks We assumed 4 classes (C1 rice and dish C2 misosoup C3 noodles and C4 shrimp) as food types

Figure 8 shows time series of sensing data for a set ofeating actions in experiment 1 The eating actions are cropaction and intake action In crop action 6-axis motionsensors detect movement (open and close) of the chopsticksIn intake action sensors detect movement of the arm fromthe dish to the mouth Each graph in Figure 8 shows certaincorrelation with both actions

Figure 9 shows a typical signal for each food in exper-iment 2 In this experiment we calculate the recognitionaccuracy of food type classification by using Random Forestclassier ofWeka Tables 5 and 6 show the classification resultsThe average recognition accuracy was 896 If we did nothave a SenStick it would be hard to develop the sensorizedchopsticks that can be used in the actual environment

42 Daily Activity Monitoring by a Sensorized Belt Recentlycorpulent people are rapidly increasing and about 700 mil-lion people belong to the obesity group [18] Nowadays manyoffice workers use computers heavily and their sitting time

10 Journal of Sensors

Step 1 crop action Step 2 intake action

Lower

Upper

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

X

Y

Z

Synthetic

X

Y

Z

Synthetic

X

Y

Z

Synthetic

X

Y

Z

Synthetic

minus250

minus150

minus50

50

150

250

Ang

ular

velo

city

(rad

s)

minus20

minus10

01020

Acce

lera

tion

(ms

2)

minus250

minus150

minus50

50

150

250

Ang

ular

velo

city

(rad

s)

minus20

minus10

01020

Acce

lera

tion

(ms

2)

Open1 Close2 Raise3 Eat4 Drop5

1 2 3 4 5

Figure 8 Time series of sensing data in eating activity

in office increases Keeping incorrect postures during longworking time increases the risk of obesity of office workersTherefore it is necessary tomonitor the daily activity of officeworkers for correcting the bad habits

Motivated by this we have developed a sensorized beltcalled Waiston Belt [8] which aims to monitor a userrsquosdaily activities for maintaining health As products of belt-type wearable devices Welt [19] and Belty [20] are on saleThese provide the function of waist measurement activity

tracking and step counting However API for obtainingsensing data is not provided and it is not suitable for researchpurposes Generally it is hard for ordinary researchers toembed sensors battery BLE and vibrator into a small buckle-size case that can be worn in everyday life Nam et al [21]have proposed a belt-attached device for monitoring postureHowever the device has not reached a sufficient level to beused on a daily basis because of exposed wiring and uncoolappearance However by extending SenStick we succeeded

Journal of Sensors 11

C1

C2 C3

C4

Typical signal of each food

C1 rice amp dish C2 miso soup C3 noodles C4 shrimp

400

200

0

minus200

minus400

Figure 9 Eating activity recognition by sensorized chopsticks

SenStick

Waiston Belt

Figure 10 Waiston Belt based on SenStick

Table 5 Experimental result (food type)

Food type Number ofpieces of data Precision Recall 119865-Measure

C1 rice and dish 3011 0894 0945 0919C2 miso soup 2187 0907 0841 0873C3 noodles 2761 088 0896 0888C4 shrimp 2091 0907 088 0893

0896 0896 0895

to develop a stylish prototype of Waiston Belt shown inFigure 10 quickly Since the battery life of SenStick is 15 hourswith a small battery (60mAh) we can sense continuously inour daily lives As shown in Figure 11 it is possible to estimatevarious usersrsquo contexts (How much did you walk Are youusing the stairs Is posture good at work) by analyzing timeseries of sensing data obtained from the sensorized belt

An important point in this use case is that usersrsquo contextscan be estimated by combining multiple sensing data For

Table 6 Confusion matrix of food type recognition

PredictedC1 C2 C3 C4

ActualC1 rice and dish 094 004 001 000C2 miso soup 012 084 004 000C3 noodles 002 002 090 006C4 shrimp 001 001 011 088

example climbing stairs is estimated from the changes ofacceleration and atmospheric pressure The change of thehumidity data will be helpful to detect that a user enteredan air-conditioned room A SenStick makes it possible toeasily carry out research on behavior recognition with acombination of such multiple sensors

We introduce basic experiments about the posture recog-nition during deskwork by using this beltThe purpose of thisexperiment is to identify the posture type from the attitude

12 Journal of Sensors

Enter the lab

HumidityTemp

Humidity dropping

Pressure dropping

Working

Walking WalkingClimbing stairs Sitting (good posture) Sitting (bad posture)

AccsXAccsYAccsZ

60

40

20

1003

1002

1001

1000

999

25

2

15

1

05

0

minus15

minus1

minus05

Air pressure

Figure 11 Time series of sensing data in daily activity

C1 good posture C2 bad posture 1

C3 bad posture 2 C4 bad posture 3

Figure 12 Time series of sensing data in posture monitoring

angle of the belt calculated from 3 axial acceleration sensorsWe set the sensing rate as 10Hz and collected 6000 samples(10min) for each posture We assumed 4 classes (C1 goodposture C2 bad posture 1 C3 bad posture 2 and C4 badposture 3) as shown in Figure 12

In this experiment we calculated the recognition accu-racy of posture type classification by using Random Forestclassifier of Weka Tables 7 and 8 show the classificationresults The average recognition accuracy was 997 Inthe future based on the result of posture estimation we

Journal of Sensors 13

Sensorized glasses

Figure 13 Sensorized glasses

Table 7 Experimental result (posture type)

Posture type Number ofpieces of data Precision Recall 119865-Measure

C1 good posture 6000 1000 1000 1000C2 bad posture 1 6000 0994 0994 0994C3 bad posture 2 6000 0994 0994 0994C4 bad posture 3 6000 0999 0999 0999

0997 0997 0997

will develop a software application that suggests correctinghisher bad posture to a right posture during desk work

43 Indoor Localization by Sensorized Glasses We have pro-posed an indoor localization system using illuminance-basedtrilateration method [22] In this system we assume that theperson wears glasses having an illuminance sensor as shownin Figure 13 and controllable light sources are deployed in thetarget environment Our system controls lights andmeasuresthe variation of illuminance at the glasses As shown inFigure 14 the distances (1198891ndash1198893) from each light (1198971ndash1198973) to theuser (119906) are estimated from the distance-illumination modelconstructed in advance and userrsquos position is estimated basedon trilateration method using estimated distances

As a result of an experiment where three controllablelight sources were deployed in a room we confirmed thatour system could estimate the position within 05m erroron average By using SenStick we could easily develop thesensorized glasses equipped with illuminance sensor

44 Sensorized Airsoft Gun We have developed the sen-sorized airsoft gun for activity recognition in airsoft gameas shown in Figure 15 Airsoft game is a sport in whichparticipants eliminate opponents by hitting each other usingreplica weapons called airsoft guns The graph of a typicalsignal from the sensorized airsoft gun is shown in Figure 16A user can count the number of shots of a ball by attachingSenStick to his airsoft gun

45 Another Example Studentrsquos Unique Achievements andSurvey Results SenStick is also used in a lecture of IoT

Table 8 Confusion matrix of posture type recognition

PredictedC1 C2 C3 C4

ActualC1 good posture 1000 0000 0000 0000C2 bad posture 1 0000 0994 0006 0001C3 bad posture 2 0000 0006 0994 0000C4 bad posture 3 0000 0001 0000 0999

prototyping Students realize their unique IoT devices easilyby using SenSticks Here we introduce some of the developedIoT devices and survey results about the usefulness ofSenStick answered by students

Figure 17 shows a sensorized pencil with two SenSticksThe sensorized pencil is developed to recognize the charac-ters written by this pencil Even though two SenSticks areembedded to detect precise movement of pencil the size ofthe pencil is reasonable

Figure 18 shows a sensorized name tag anddigital signageSince we changed the battery from 60mAh to 900mAh thetag keeps running more than one week It aims to recordemployeesrsquo or studentsrsquo daily activities to support their healthThe digital signage informs the user who wears a sensorizedname tag about the userrsquos activity data when the user standsin front of it

Figure 19 shows a sensorized spoon used in the digitalfish catching game for children In this game the sensorizedspoon detects the scooping movement of the hand Thuschildren can catch the digital fish displayed by the projectionmapping by using sensorized spoonThis systemwas actuallyused at the universityrsquos open campus event and around100 children enjoyed this game and showed interest in IoTtechnology

The survey results obtained from the ten graduate stu-dents (ninemales and one female) who developed prototypesas mentioned above are shown in Table 9 A questionnaireis a five-grade form (5 strongly agree 4 agree a little 3neither agree nor disagree 2 disagree a little and 1 stronglydisagree) From the results of questions 1 and 2 manyparticipants think that SenStick platform is useful for IoT

14 Journal of Sensors

Position

Distance illumination model

Using trilateration method

a

Light 3

Light 2

Light 1

L = minus421d + 1496

L = minus063d + 501

05 1 15 2 25 3 35 4 450Distance between the illuminance sensor and the lighting device (m)

02468

1012141618

Illum

inan

ce v

alue

(lux

)

Distanceu

d1

d2

d3

l1 p1 x1

l3 p3 x3

l2 p2 x2

Figure 14 Indoor localization by sensorized glasses

Sensorized airsoft gun

Figure 15 Sensorized airsoft gun

research and IoT education Also the results of question 3show that many students are interested in the SenStick andwant to use it again From the results of questions 4 and 5we can see that usability and specification of SenStick arerelatively high There were opinions about the usefulness ofSenStick platform such as ldquoI could quickly try out ideas of IoTprototypingrdquo ldquoIt was easy to collect sensing datardquo and ldquoSinceSenStick is small it was easily attached tomost of the thingsrdquoOn the other hand there were opinions about improvements

such as ldquoI want to use higher sensing frequency (50ndash100Hz)rdquoldquoI want to process a sensing data on SenStickrdquo and ldquoI want tomodify the firmware itself depending on the application ofthe IoT devicerdquo

5 Conclusion

In this paper we have introduced a smart and comprehensivesensing platform called SenStick which is composed of

Journal of Sensors 15

Typical signal of airso gun

Hold a gun Ready

Shoot

1 2 3 4 5 6 70

00

30

60

91

21

51

82

12

42

73

03

33

63

94

24

54

85

15

45

76

06

36

66

97

27

57

88

18

48

79

09

39

69

910

210

510

811

111

411

712

012

312

612

913

2

minus600

minus400

minus200

0

200

400

600

800

acc_xacc_yacc_z

gyro_xgyro_y

acc_synthesis gyro_zgyro_synthesis

Figure 16 Time series of sensing data in shooting an airsoft gun

Sensorized pencil

Figure 17 Sensorized pencil

Sensorized name tagVisualization of sensor data

Figure 18 Sensorized name tag

Sensorized spoon Digital fishing game

Figure 19 Sensorized spoon

Table 9 Questionnaire results about SenStick

Question contents ScoreQ1 Do you think that SenStick will be useful for IoTresearch 49

Q2 Do you think that SenStick will be useful for IoTeducation 47

Q3 Do you want to use SenStick again 49Q4 Do you think that SenStick was easy to use 41Q5 Are you satisfied with specification of SenStick 41

hardware software and 3D case data The main purpose ofour platform is to simplify the sensorization of our personalbelongings

The hardware of SenStick is maybe one of the worldrsquossmallest sensor boards which includes 8 kinds of sensorsand BLE In addition to the hardware we also developedsupport software for managing monitoring recording andviewing the hardware By providing 3D case data all theresearchers can measure every tiny activity in daily life easilyand efficiently For an educational use it is also useful forunderstanding sensors and prototyping the desired systemincluding tiny censored things

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

This work is supported in part by JST PRESTO and JSPSKAKENHI (Grant no 17J10021) and ldquoResearch and Devel-opment of Innovative Network Technologies to Create theFuturerdquo the Commissioned Research of National Instituteof Information and Communications Technology (NICT)Japan

16 Journal of Sensors

References

[1] O Brdiczka M Langet J Maisonnasse and J L CrowleyldquoDetecting human behavior models from multimodal observa-tion in a smart homerdquo IEEE Transactions on Automation Scienceand Engineering vol 6 no 4 pp 588ndash597 2009

[2] J Chen A Kam J Zhang N Liu and L Shue ldquoBathroomactivity monitoring based on soundrdquo in Pervasive ComputingThird International Conference PERVASIVE 2005 MunichGermanyMay 8-13 2005 Proceedings vol 3468 ofLectureNotesin Computer Science pp 47ndash61 Springer Berlin Germany2002

[3] G Bauer K Stockinger and P Lukowicz ldquoRecognizing theUse-Mode of Kitchen Appliances from Their Current Consump-tionrdquo in Smart Sensing and Context vol 5741 of Lecture Notesin Computer Science pp 163ndash176 Springer Berlin HeidelbergBerlin Heidelberg 2009

[4] K Ouchi and M Doi ldquoIndoor-outdoor activity recognition bya smartphonerdquo in Proceedings of the the 2012 ACM Conferencep 537 Pittsburgh Pennsylvania September 2012

[5] A Anjum and M U Ilyas ldquoActivity recognition using smart-phone sensorsrdquo in Proceedings of the IEEE 10th ConsumerCommunications and Networking Conference (CCNC rsquo13) pp914ndash919 IEEE January 2013

[6] O D Lara and M A Labrador ldquoA survey on human activityrecognition using wearable sensorsrdquo IEEE CommunicationsSurveys amp Tutorials vol 15 no 3 pp 1192ndash1209 2013

[7] ldquoJins memerdquo httpsjins-memecom[8] Y Matsuda T Ishioka T Hasegawa et al ldquoWaistonBelt A belt

for monitoring your real abdominal circumference foreverrdquo inProceedings of the ACM International Joint Conference on Perva-sive and Ubiquitous Computing and the 2015 ACM InternationalSymposium on Wearable Computers UbiComp and ISWC 2015pp 329ndash332 September 2015

[9] M Kurahashi K Murao T Terada and M Tsukamoto ldquoPer-sonal identification system based on rotation of toilet paperrollsrdquo in Proceedings of the IEEE International Conference onPervasive Computing and CommunicationsWorkshops (PerComWorkshops rsquo17) pp 521ndash526 IEEE Kona Hawaii USA March2017

[10] ldquoHapiforkrdquo httpswwwhapicom[11] Y Arakawa ldquoSenStick Sensorize every thingsrdquo in Proceedings of

the ACM International Joint Conference on Pervasive and Ubiq-uitous Computing and the 2015 ACM International SymposiumonWearable Computers UbiComp and ISWC 2015 pp 349ndash352September 2015

[12] Y Nakamura T Kanehira Y Arakawa and K YasumotoldquoSenStick 2 Ultra tiny all-in-one sensor with wireless chargingrdquoinProceedings of the 2016ACM International Joint Conference onPervasive and Ubiquitous Computing UbiComp 2016 pp 337ndash340 September 2016

[13] ldquoSensortag cc2541 (texas instruments)rdquo httpwwwticomtoolcc2541dk-sensor

[14] ldquoIot smart modulerdquo httpwwwalpscomjiotsmart[15] ldquoTsnd121 (atr-promotions)rdquo httpwwwatr-pcomproducts

TSND121html[16] ldquoWax9 (axivity)rdquo httpaxivitycomproductwax9[17] Uk biobank ldquoUk biobankrdquo httpwwwukbiobankacuk[18] J C Seidell and J Halberstadt ldquoThe global burden of obesity

and the challenges of preventionrdquo Annals of Nutrition andMetabolism vol 66 supplement 2 pp 7ndash12 2015

[19] ldquoWeltThe smart belt for fashion and healthrdquo httpswwwwelt-corpcom

[20] ldquoBelty power The belt your really deserverdquo httpswwwbeltyco

[21] H Nam J-H Kim and J-I Kim ldquoSmart Belt A wearabledevice for managing abdominal obesityrdquo in Proceedings of theInternational Conference on Big Data and Smart ComputingBigComp 2016 pp 430ndash434 January 2016

[22] K Moriya M Fujimoto Y Arakawa and K Yasumoto ldquoIndoorlocalization based on distance-illuminance model and activecontrol of lighting devicesrdquo in Proceedings of the 2016 Interna-tional Conference on Indoor Positioning and Indoor NavigationIPIN 2016 pp 1ndash6 October 2016

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal of

Volume 201

Submit your manuscripts athttpswwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

Page 6: SenStick: Comprehensive Sensing Platform with an Ultra ...downloads.hindawi.com/journals/js/2017/6308302.pdf · ResearchArticle SenStick: Comprehensive Sensing Platform with an Ultra

6 Journal of Sensors

(32-)

Peripheral

GATT server

SenStick Control Service

Metadata Read Service

Read-write

Data notication

Flashmemory

(0 acceleration 1 gyro 2 magnetic 3 light

4 UV 5 temperaturehumidity 6 pressure)

GATT client

Central

~Sensor Service (0 6)

Figure 3 Generic Attribute (GATT) profile of SenStick

Table 3 BLE communication specification

Type of content SpecAdvertising packet Complete UUID flagScan response Local name (SenStick)Advertising packet interval 100ms (30 s)rarr1000ms (150 s)rarrsleepConnection interval max 80msmin 20msSupervision timeout 4000msSlave latency 0

Basically all the sensing data will be notified to a smart-phone in real time up to a transmission capacity of BLE Ifthe amount of sensing data exceeds the transmission capacitya part of sensing data will be dropped Therefore the real-time sensing data reading function is used formonitoring thestatus or for an interactive application that does not requireprecise data For precise data acquisition the log data readingfunction which guarantees the data reading order is usedThe communication specifications of BLE are described inTable 3

As shown in Table 4 the fixed capacities of a flashmemory are allocated to each sensorThe number of samplesindicates the total number of samples that can be recorded bythe allocated capacities

SenStick stops logging when the number of samples ofone sensor exceeds the storage capacity allocated for it Themaximum recording time of acceleration gyroscope andmagnetic sensor is 4 hours 40 minutes at sampling intervalof 10 milliseconds (sampling frequency 100Hz) and 14 hours10 minutes at sampling period of 30 milliseconds (samplingfrequency 33Hz) The maximum recording time of othersensors is 9 hours 26 minutes at sampling period of 200milliseconds (sampling frequency 5Hz)

32 Software We provide mobile applications for iOS andAndroid and a library for nodejs platform Common func-tions of them are operating function parameter settingfunction and real-time data view function An additional

Table 4 Allocated storage capacity per sensor

Amount of allocatedstorage capacity Samples

Acceleration 102Mbytes 17M samplesGyro 102Mbytes 17M samplesMagnetic 102Mbytes 17M samplesIlluminance 340KB 170K samplesUV (UV) 340KB 170K samplesHumidity 170KB 170K samplesAir temperature 170KB 170K samples

function of iOS application is a firmware update functionThose of Android application and nodejs applications areground truth video recording function and an ability to con-nect multiple SenSticks respectively Since all these softwareapplications are shared on GitHub as an own source softwareall the users can create their application based on their ownpurposes

Communication Protocol Here we explain the communica-tion protocol between SenStick and softwareThe explanationis based on ldquonode-SenStickrdquo which is our developed libraryfor nodejs Figure 4 shows a protocol sequence betweenSenStick and the central terminal (node-SenStick on Mac)

(I) Discovery First of all the application needs to find thesurrounding SenStick by using SenStickdiscovery() methodin our library When SenStickdiscovery() method finds aSenStick a callback function with an instance of SenStickobject containing the SenStickrsquos UUID and LocalName asarguments is called

(II) Connect If the target SenStick is found by the discoveryprocess the application is going to establish a connec-tion with it by calling SenStickprototypeconnectAndSetUp()method After establishing the connection this methodacquires the information necessary for operation from thetarget SenStick

Journal of Sensors 7

Central

Node-SenStick SenStick

SenStickdiscovery()

SenStickprototypeconnectAndSetUp()

Establish connection

SenStickprototypewriteSensorMeasurementCong()

Reect setting

SenStickprototypestartSensingAndLogging()

SenStickprototypenotifySensor()Start sensing

Select sensor to notify

Sensing data

SenStickprototypestopSensingAndLogging()

Stop sensing

Enable log notication

Select log data to notify

SenStickprototypewriteSensorLogReadoutTargetID()

Logging data

SenStickprototypedisconnect()

Disconnect

SenStickprototypenotifySensorLogData()

Periperal

(I) Discovery

(II) Connect

(III) Setting

(V) Obtaining log

(VI) Disconnect

(IV) Start sensingamp

RT receiving

Figure 4 Communication sequence between SenStick and node-SenStick

(III) Setting After establishing the connection the appli-cation sets three parameters (operation mode samplinginterval and measurement range) for each sensor by usingSenStickprototypewriteSensorMeasurementConfig()methodThe operation mode is a parameter that sets valida-tioninvalidation of sensing and logging of each sensor Thesampling interval and measurement range are parameters ofeach sensorwhichwill be changed by the sensing purpose andapplications

(IV) Start Sensing and Real-Time Data Receiving When Sen-StickprototypestartSensingAndLogging() method is calledSenStick starts sensing and logging Only valid sensorsrsquodata are transmitted via BLE and also recorded into aflash memory ID of each log is automatically assignedDuring sensing SenStick notifies sensing data in realtime To receive this notification the application callsSenStickprototypenotifySensor() method (Sensor representseach sensor name) and activates the notification for

8 Journal of Sensors

(i) Frequency(ii) Range

Device select Main view Settings Firmware update

Real-time view Log list in SenStick Download from SenStick

Selected sensorSensor name

Sensing frequency

Range of y-axis

Figure 5 iOS application for SenStick

the sensor When a notification of sensing data isreceived sensorChange event will be issued If Sen-StickprototypestopSensingAndLogging() method is calledSenStick stops sensing and logging

(V) Log Data Reading To read the log data recordedin the flash memory to the application the applicationcalls SenStickprototypenotifySensorLogData()method (Sen-sor represents each sensor name) and enables log datanotification If the application specifies a valid log ID bycalling SenStickprototypewriteSensorLogReadoutTargetID()method SenStick starts informing the sensing data recordedin the log sequentially To receive notification of log datasensorLogDataReceived event will be issued By receiving thisnotification by the application it is possible to acquire the logdata recorded in the memory

(VI) Disconnect After finishing the data acquisition Sen-Stickprototypedisconnect() method will be called to releasethe connection of SenStick And SenStick will begin advertis-ing again

User InterfaceWe explain the user interface of the applicationbased on iOS version Figure 5 shows a sophisticated mobileapplication for SenStick As shown in Figure 5 the userselects target SenStick from devices listThen the applicationshows the main view In the main view the user can selectsensors to be acquired For immediate data monitoring allthe received data via BLE can be shown in the graph view inreal time The application can control all the settings of eachsensor such as sample frequency and sensing range And alsoit has firmware update function Sensing data stored in theflash memory of SenStick is viewed from log list and it can be

downloaded to the smartphone in CSV formatThis functionprevents data loss caused by BLE communication

33 3D Case Data In order to make sensorized things anattachment case for mounting the SenStick is required Wedesigned the basic 3D case of SenStick By extending thisbasic case users can design derivative cases for chopsticksglasses and so on Actually our laboratory has succeeded indesigning cases for several things as shown in Figure 6 Allof these cases data are shared on our community site Eachcase is designed in a few minutes thanks to the basic caseFor example SenStick case for glasses is made by combiningbasic case of SenStick and open 3D data of glasses distributedby i Design Studio In addition due to the popularizationof 3D printers in the world and enhancement of tools andreferences for 3D modeling all the users can print out theirdesired SenStick cases by themselves and attach our sensor toeverything

4 Case Studies of SenStick

SenStick an ultra small sensor can be embedded into varioustiny things in our daily life Our first target is our dailybelongings such as glasses chopsticks and pens becausesensorization of daily belongings is good to intuitively under-standwhat can be sensed andwhich sensor is usefuleffectiveSenStick can upgrade normal glasses to intelligent oneswhichcan monitor a head movement posture and the amount ofsunlight received If SenStick is embedded into chopsticksand pens we can observe eating activities and handwritingactivities respectively In the future we will embed it into astick for elderlymonitoring sports gearsmusical instrument

Journal of Sensors 9

Chopsticks Tooth brush Glasses Umbrella Name card Cookware

A user can design original case data in a few minutes

Basic 3D case of SenStick

Figure 6 3D case data of SenStick

Sensorized

Chopsticks

Figure 7 Eating activity recognition by sensorized chopsticks

kitchen utensils animals stuffed toy a remote control andany kind of things that can be sensing targets In this sectionwe introduce some case studies results utilizing SenStick

41 Eating Behavior Recognition by Sensorized Chopsticks Inmany cases East Asian people eat meals with chopsticksBy analyzing the data obtained from chopsticks there is apossibility that we can estimate the eating habits includinganswers for questions like ldquohow did you eatrdquo and ldquowhatdid you eatrdquo and so forth These data are very useful formaking healthcare applications and leading a healthy lifeThis is one reason why we develop the sensorized chopsticksusing SenStick shown in Figure 7

Research goals of the eating behavior recognition by sen-sorized chopsticks are as follows G1 (behavior) recognizingthe timing and speed and detecting ldquocroprdquo and ldquointakerdquoaction G2 (food type) classifying food content like ricenoodles and so forth from the eating behavior and G3(person) identifying an individual from using chopsticks Inthis paper we show the research results on G1 and G2

We conducted basic experiments to recognize the eatingbehavior (G1 and G2) We used acceleration and gyroscopedata measured by SenStick where the sensing rate is 10HzThe purpose of experiment 1 is to understand the basic

characteristics of sensing data from sensorized chopstickwhen a participant ate chips The purpose of experiment 2 isto identify the food type from the difference in movement ofchopsticks We assumed 4 classes (C1 rice and dish C2 misosoup C3 noodles and C4 shrimp) as food types

Figure 8 shows time series of sensing data for a set ofeating actions in experiment 1 The eating actions are cropaction and intake action In crop action 6-axis motionsensors detect movement (open and close) of the chopsticksIn intake action sensors detect movement of the arm fromthe dish to the mouth Each graph in Figure 8 shows certaincorrelation with both actions

Figure 9 shows a typical signal for each food in exper-iment 2 In this experiment we calculate the recognitionaccuracy of food type classification by using Random Forestclassier ofWeka Tables 5 and 6 show the classification resultsThe average recognition accuracy was 896 If we did nothave a SenStick it would be hard to develop the sensorizedchopsticks that can be used in the actual environment

42 Daily Activity Monitoring by a Sensorized Belt Recentlycorpulent people are rapidly increasing and about 700 mil-lion people belong to the obesity group [18] Nowadays manyoffice workers use computers heavily and their sitting time

10 Journal of Sensors

Step 1 crop action Step 2 intake action

Lower

Upper

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

X

Y

Z

Synthetic

X

Y

Z

Synthetic

X

Y

Z

Synthetic

X

Y

Z

Synthetic

minus250

minus150

minus50

50

150

250

Ang

ular

velo

city

(rad

s)

minus20

minus10

01020

Acce

lera

tion

(ms

2)

minus250

minus150

minus50

50

150

250

Ang

ular

velo

city

(rad

s)

minus20

minus10

01020

Acce

lera

tion

(ms

2)

Open1 Close2 Raise3 Eat4 Drop5

1 2 3 4 5

Figure 8 Time series of sensing data in eating activity

in office increases Keeping incorrect postures during longworking time increases the risk of obesity of office workersTherefore it is necessary tomonitor the daily activity of officeworkers for correcting the bad habits

Motivated by this we have developed a sensorized beltcalled Waiston Belt [8] which aims to monitor a userrsquosdaily activities for maintaining health As products of belt-type wearable devices Welt [19] and Belty [20] are on saleThese provide the function of waist measurement activity

tracking and step counting However API for obtainingsensing data is not provided and it is not suitable for researchpurposes Generally it is hard for ordinary researchers toembed sensors battery BLE and vibrator into a small buckle-size case that can be worn in everyday life Nam et al [21]have proposed a belt-attached device for monitoring postureHowever the device has not reached a sufficient level to beused on a daily basis because of exposed wiring and uncoolappearance However by extending SenStick we succeeded

Journal of Sensors 11

C1

C2 C3

C4

Typical signal of each food

C1 rice amp dish C2 miso soup C3 noodles C4 shrimp

400

200

0

minus200

minus400

Figure 9 Eating activity recognition by sensorized chopsticks

SenStick

Waiston Belt

Figure 10 Waiston Belt based on SenStick

Table 5 Experimental result (food type)

Food type Number ofpieces of data Precision Recall 119865-Measure

C1 rice and dish 3011 0894 0945 0919C2 miso soup 2187 0907 0841 0873C3 noodles 2761 088 0896 0888C4 shrimp 2091 0907 088 0893

0896 0896 0895

to develop a stylish prototype of Waiston Belt shown inFigure 10 quickly Since the battery life of SenStick is 15 hourswith a small battery (60mAh) we can sense continuously inour daily lives As shown in Figure 11 it is possible to estimatevarious usersrsquo contexts (How much did you walk Are youusing the stairs Is posture good at work) by analyzing timeseries of sensing data obtained from the sensorized belt

An important point in this use case is that usersrsquo contextscan be estimated by combining multiple sensing data For

Table 6 Confusion matrix of food type recognition

PredictedC1 C2 C3 C4

ActualC1 rice and dish 094 004 001 000C2 miso soup 012 084 004 000C3 noodles 002 002 090 006C4 shrimp 001 001 011 088

example climbing stairs is estimated from the changes ofacceleration and atmospheric pressure The change of thehumidity data will be helpful to detect that a user enteredan air-conditioned room A SenStick makes it possible toeasily carry out research on behavior recognition with acombination of such multiple sensors

We introduce basic experiments about the posture recog-nition during deskwork by using this beltThe purpose of thisexperiment is to identify the posture type from the attitude

12 Journal of Sensors

Enter the lab

HumidityTemp

Humidity dropping

Pressure dropping

Working

Walking WalkingClimbing stairs Sitting (good posture) Sitting (bad posture)

AccsXAccsYAccsZ

60

40

20

1003

1002

1001

1000

999

25

2

15

1

05

0

minus15

minus1

minus05

Air pressure

Figure 11 Time series of sensing data in daily activity

C1 good posture C2 bad posture 1

C3 bad posture 2 C4 bad posture 3

Figure 12 Time series of sensing data in posture monitoring

angle of the belt calculated from 3 axial acceleration sensorsWe set the sensing rate as 10Hz and collected 6000 samples(10min) for each posture We assumed 4 classes (C1 goodposture C2 bad posture 1 C3 bad posture 2 and C4 badposture 3) as shown in Figure 12

In this experiment we calculated the recognition accu-racy of posture type classification by using Random Forestclassifier of Weka Tables 7 and 8 show the classificationresults The average recognition accuracy was 997 Inthe future based on the result of posture estimation we

Journal of Sensors 13

Sensorized glasses

Figure 13 Sensorized glasses

Table 7 Experimental result (posture type)

Posture type Number ofpieces of data Precision Recall 119865-Measure

C1 good posture 6000 1000 1000 1000C2 bad posture 1 6000 0994 0994 0994C3 bad posture 2 6000 0994 0994 0994C4 bad posture 3 6000 0999 0999 0999

0997 0997 0997

will develop a software application that suggests correctinghisher bad posture to a right posture during desk work

43 Indoor Localization by Sensorized Glasses We have pro-posed an indoor localization system using illuminance-basedtrilateration method [22] In this system we assume that theperson wears glasses having an illuminance sensor as shownin Figure 13 and controllable light sources are deployed in thetarget environment Our system controls lights andmeasuresthe variation of illuminance at the glasses As shown inFigure 14 the distances (1198891ndash1198893) from each light (1198971ndash1198973) to theuser (119906) are estimated from the distance-illumination modelconstructed in advance and userrsquos position is estimated basedon trilateration method using estimated distances

As a result of an experiment where three controllablelight sources were deployed in a room we confirmed thatour system could estimate the position within 05m erroron average By using SenStick we could easily develop thesensorized glasses equipped with illuminance sensor

44 Sensorized Airsoft Gun We have developed the sen-sorized airsoft gun for activity recognition in airsoft gameas shown in Figure 15 Airsoft game is a sport in whichparticipants eliminate opponents by hitting each other usingreplica weapons called airsoft guns The graph of a typicalsignal from the sensorized airsoft gun is shown in Figure 16A user can count the number of shots of a ball by attachingSenStick to his airsoft gun

45 Another Example Studentrsquos Unique Achievements andSurvey Results SenStick is also used in a lecture of IoT

Table 8 Confusion matrix of posture type recognition

PredictedC1 C2 C3 C4

ActualC1 good posture 1000 0000 0000 0000C2 bad posture 1 0000 0994 0006 0001C3 bad posture 2 0000 0006 0994 0000C4 bad posture 3 0000 0001 0000 0999

prototyping Students realize their unique IoT devices easilyby using SenSticks Here we introduce some of the developedIoT devices and survey results about the usefulness ofSenStick answered by students

Figure 17 shows a sensorized pencil with two SenSticksThe sensorized pencil is developed to recognize the charac-ters written by this pencil Even though two SenSticks areembedded to detect precise movement of pencil the size ofthe pencil is reasonable

Figure 18 shows a sensorized name tag anddigital signageSince we changed the battery from 60mAh to 900mAh thetag keeps running more than one week It aims to recordemployeesrsquo or studentsrsquo daily activities to support their healthThe digital signage informs the user who wears a sensorizedname tag about the userrsquos activity data when the user standsin front of it

Figure 19 shows a sensorized spoon used in the digitalfish catching game for children In this game the sensorizedspoon detects the scooping movement of the hand Thuschildren can catch the digital fish displayed by the projectionmapping by using sensorized spoonThis systemwas actuallyused at the universityrsquos open campus event and around100 children enjoyed this game and showed interest in IoTtechnology

The survey results obtained from the ten graduate stu-dents (ninemales and one female) who developed prototypesas mentioned above are shown in Table 9 A questionnaireis a five-grade form (5 strongly agree 4 agree a little 3neither agree nor disagree 2 disagree a little and 1 stronglydisagree) From the results of questions 1 and 2 manyparticipants think that SenStick platform is useful for IoT

14 Journal of Sensors

Position

Distance illumination model

Using trilateration method

a

Light 3

Light 2

Light 1

L = minus421d + 1496

L = minus063d + 501

05 1 15 2 25 3 35 4 450Distance between the illuminance sensor and the lighting device (m)

02468

1012141618

Illum

inan

ce v

alue

(lux

)

Distanceu

d1

d2

d3

l1 p1 x1

l3 p3 x3

l2 p2 x2

Figure 14 Indoor localization by sensorized glasses

Sensorized airsoft gun

Figure 15 Sensorized airsoft gun

research and IoT education Also the results of question 3show that many students are interested in the SenStick andwant to use it again From the results of questions 4 and 5we can see that usability and specification of SenStick arerelatively high There were opinions about the usefulness ofSenStick platform such as ldquoI could quickly try out ideas of IoTprototypingrdquo ldquoIt was easy to collect sensing datardquo and ldquoSinceSenStick is small it was easily attached tomost of the thingsrdquoOn the other hand there were opinions about improvements

such as ldquoI want to use higher sensing frequency (50ndash100Hz)rdquoldquoI want to process a sensing data on SenStickrdquo and ldquoI want tomodify the firmware itself depending on the application ofthe IoT devicerdquo

5 Conclusion

In this paper we have introduced a smart and comprehensivesensing platform called SenStick which is composed of

Journal of Sensors 15

Typical signal of airso gun

Hold a gun Ready

Shoot

1 2 3 4 5 6 70

00

30

60

91

21

51

82

12

42

73

03

33

63

94

24

54

85

15

45

76

06

36

66

97

27

57

88

18

48

79

09

39

69

910

210

510

811

111

411

712

012

312

612

913

2

minus600

minus400

minus200

0

200

400

600

800

acc_xacc_yacc_z

gyro_xgyro_y

acc_synthesis gyro_zgyro_synthesis

Figure 16 Time series of sensing data in shooting an airsoft gun

Sensorized pencil

Figure 17 Sensorized pencil

Sensorized name tagVisualization of sensor data

Figure 18 Sensorized name tag

Sensorized spoon Digital fishing game

Figure 19 Sensorized spoon

Table 9 Questionnaire results about SenStick

Question contents ScoreQ1 Do you think that SenStick will be useful for IoTresearch 49

Q2 Do you think that SenStick will be useful for IoTeducation 47

Q3 Do you want to use SenStick again 49Q4 Do you think that SenStick was easy to use 41Q5 Are you satisfied with specification of SenStick 41

hardware software and 3D case data The main purpose ofour platform is to simplify the sensorization of our personalbelongings

The hardware of SenStick is maybe one of the worldrsquossmallest sensor boards which includes 8 kinds of sensorsand BLE In addition to the hardware we also developedsupport software for managing monitoring recording andviewing the hardware By providing 3D case data all theresearchers can measure every tiny activity in daily life easilyand efficiently For an educational use it is also useful forunderstanding sensors and prototyping the desired systemincluding tiny censored things

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

This work is supported in part by JST PRESTO and JSPSKAKENHI (Grant no 17J10021) and ldquoResearch and Devel-opment of Innovative Network Technologies to Create theFuturerdquo the Commissioned Research of National Instituteof Information and Communications Technology (NICT)Japan

16 Journal of Sensors

References

[1] O Brdiczka M Langet J Maisonnasse and J L CrowleyldquoDetecting human behavior models from multimodal observa-tion in a smart homerdquo IEEE Transactions on Automation Scienceand Engineering vol 6 no 4 pp 588ndash597 2009

[2] J Chen A Kam J Zhang N Liu and L Shue ldquoBathroomactivity monitoring based on soundrdquo in Pervasive ComputingThird International Conference PERVASIVE 2005 MunichGermanyMay 8-13 2005 Proceedings vol 3468 ofLectureNotesin Computer Science pp 47ndash61 Springer Berlin Germany2002

[3] G Bauer K Stockinger and P Lukowicz ldquoRecognizing theUse-Mode of Kitchen Appliances from Their Current Consump-tionrdquo in Smart Sensing and Context vol 5741 of Lecture Notesin Computer Science pp 163ndash176 Springer Berlin HeidelbergBerlin Heidelberg 2009

[4] K Ouchi and M Doi ldquoIndoor-outdoor activity recognition bya smartphonerdquo in Proceedings of the the 2012 ACM Conferencep 537 Pittsburgh Pennsylvania September 2012

[5] A Anjum and M U Ilyas ldquoActivity recognition using smart-phone sensorsrdquo in Proceedings of the IEEE 10th ConsumerCommunications and Networking Conference (CCNC rsquo13) pp914ndash919 IEEE January 2013

[6] O D Lara and M A Labrador ldquoA survey on human activityrecognition using wearable sensorsrdquo IEEE CommunicationsSurveys amp Tutorials vol 15 no 3 pp 1192ndash1209 2013

[7] ldquoJins memerdquo httpsjins-memecom[8] Y Matsuda T Ishioka T Hasegawa et al ldquoWaistonBelt A belt

for monitoring your real abdominal circumference foreverrdquo inProceedings of the ACM International Joint Conference on Perva-sive and Ubiquitous Computing and the 2015 ACM InternationalSymposium on Wearable Computers UbiComp and ISWC 2015pp 329ndash332 September 2015

[9] M Kurahashi K Murao T Terada and M Tsukamoto ldquoPer-sonal identification system based on rotation of toilet paperrollsrdquo in Proceedings of the IEEE International Conference onPervasive Computing and CommunicationsWorkshops (PerComWorkshops rsquo17) pp 521ndash526 IEEE Kona Hawaii USA March2017

[10] ldquoHapiforkrdquo httpswwwhapicom[11] Y Arakawa ldquoSenStick Sensorize every thingsrdquo in Proceedings of

the ACM International Joint Conference on Pervasive and Ubiq-uitous Computing and the 2015 ACM International SymposiumonWearable Computers UbiComp and ISWC 2015 pp 349ndash352September 2015

[12] Y Nakamura T Kanehira Y Arakawa and K YasumotoldquoSenStick 2 Ultra tiny all-in-one sensor with wireless chargingrdquoinProceedings of the 2016ACM International Joint Conference onPervasive and Ubiquitous Computing UbiComp 2016 pp 337ndash340 September 2016

[13] ldquoSensortag cc2541 (texas instruments)rdquo httpwwwticomtoolcc2541dk-sensor

[14] ldquoIot smart modulerdquo httpwwwalpscomjiotsmart[15] ldquoTsnd121 (atr-promotions)rdquo httpwwwatr-pcomproducts

TSND121html[16] ldquoWax9 (axivity)rdquo httpaxivitycomproductwax9[17] Uk biobank ldquoUk biobankrdquo httpwwwukbiobankacuk[18] J C Seidell and J Halberstadt ldquoThe global burden of obesity

and the challenges of preventionrdquo Annals of Nutrition andMetabolism vol 66 supplement 2 pp 7ndash12 2015

[19] ldquoWeltThe smart belt for fashion and healthrdquo httpswwwwelt-corpcom

[20] ldquoBelty power The belt your really deserverdquo httpswwwbeltyco

[21] H Nam J-H Kim and J-I Kim ldquoSmart Belt A wearabledevice for managing abdominal obesityrdquo in Proceedings of theInternational Conference on Big Data and Smart ComputingBigComp 2016 pp 430ndash434 January 2016

[22] K Moriya M Fujimoto Y Arakawa and K Yasumoto ldquoIndoorlocalization based on distance-illuminance model and activecontrol of lighting devicesrdquo in Proceedings of the 2016 Interna-tional Conference on Indoor Positioning and Indoor NavigationIPIN 2016 pp 1ndash6 October 2016

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal of

Volume 201

Submit your manuscripts athttpswwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

Page 7: SenStick: Comprehensive Sensing Platform with an Ultra ...downloads.hindawi.com/journals/js/2017/6308302.pdf · ResearchArticle SenStick: Comprehensive Sensing Platform with an Ultra

Journal of Sensors 7

Central

Node-SenStick SenStick

SenStickdiscovery()

SenStickprototypeconnectAndSetUp()

Establish connection

SenStickprototypewriteSensorMeasurementCong()

Reect setting

SenStickprototypestartSensingAndLogging()

SenStickprototypenotifySensor()Start sensing

Select sensor to notify

Sensing data

SenStickprototypestopSensingAndLogging()

Stop sensing

Enable log notication

Select log data to notify

SenStickprototypewriteSensorLogReadoutTargetID()

Logging data

SenStickprototypedisconnect()

Disconnect

SenStickprototypenotifySensorLogData()

Periperal

(I) Discovery

(II) Connect

(III) Setting

(V) Obtaining log

(VI) Disconnect

(IV) Start sensingamp

RT receiving

Figure 4 Communication sequence between SenStick and node-SenStick

(III) Setting After establishing the connection the appli-cation sets three parameters (operation mode samplinginterval and measurement range) for each sensor by usingSenStickprototypewriteSensorMeasurementConfig()methodThe operation mode is a parameter that sets valida-tioninvalidation of sensing and logging of each sensor Thesampling interval and measurement range are parameters ofeach sensorwhichwill be changed by the sensing purpose andapplications

(IV) Start Sensing and Real-Time Data Receiving When Sen-StickprototypestartSensingAndLogging() method is calledSenStick starts sensing and logging Only valid sensorsrsquodata are transmitted via BLE and also recorded into aflash memory ID of each log is automatically assignedDuring sensing SenStick notifies sensing data in realtime To receive this notification the application callsSenStickprototypenotifySensor() method (Sensor representseach sensor name) and activates the notification for

8 Journal of Sensors

(i) Frequency(ii) Range

Device select Main view Settings Firmware update

Real-time view Log list in SenStick Download from SenStick

Selected sensorSensor name

Sensing frequency

Range of y-axis

Figure 5 iOS application for SenStick

the sensor When a notification of sensing data isreceived sensorChange event will be issued If Sen-StickprototypestopSensingAndLogging() method is calledSenStick stops sensing and logging

(V) Log Data Reading To read the log data recordedin the flash memory to the application the applicationcalls SenStickprototypenotifySensorLogData()method (Sen-sor represents each sensor name) and enables log datanotification If the application specifies a valid log ID bycalling SenStickprototypewriteSensorLogReadoutTargetID()method SenStick starts informing the sensing data recordedin the log sequentially To receive notification of log datasensorLogDataReceived event will be issued By receiving thisnotification by the application it is possible to acquire the logdata recorded in the memory

(VI) Disconnect After finishing the data acquisition Sen-Stickprototypedisconnect() method will be called to releasethe connection of SenStick And SenStick will begin advertis-ing again

User InterfaceWe explain the user interface of the applicationbased on iOS version Figure 5 shows a sophisticated mobileapplication for SenStick As shown in Figure 5 the userselects target SenStick from devices listThen the applicationshows the main view In the main view the user can selectsensors to be acquired For immediate data monitoring allthe received data via BLE can be shown in the graph view inreal time The application can control all the settings of eachsensor such as sample frequency and sensing range And alsoit has firmware update function Sensing data stored in theflash memory of SenStick is viewed from log list and it can be

downloaded to the smartphone in CSV formatThis functionprevents data loss caused by BLE communication

33 3D Case Data In order to make sensorized things anattachment case for mounting the SenStick is required Wedesigned the basic 3D case of SenStick By extending thisbasic case users can design derivative cases for chopsticksglasses and so on Actually our laboratory has succeeded indesigning cases for several things as shown in Figure 6 Allof these cases data are shared on our community site Eachcase is designed in a few minutes thanks to the basic caseFor example SenStick case for glasses is made by combiningbasic case of SenStick and open 3D data of glasses distributedby i Design Studio In addition due to the popularizationof 3D printers in the world and enhancement of tools andreferences for 3D modeling all the users can print out theirdesired SenStick cases by themselves and attach our sensor toeverything

4 Case Studies of SenStick

SenStick an ultra small sensor can be embedded into varioustiny things in our daily life Our first target is our dailybelongings such as glasses chopsticks and pens becausesensorization of daily belongings is good to intuitively under-standwhat can be sensed andwhich sensor is usefuleffectiveSenStick can upgrade normal glasses to intelligent oneswhichcan monitor a head movement posture and the amount ofsunlight received If SenStick is embedded into chopsticksand pens we can observe eating activities and handwritingactivities respectively In the future we will embed it into astick for elderlymonitoring sports gearsmusical instrument

Journal of Sensors 9

Chopsticks Tooth brush Glasses Umbrella Name card Cookware

A user can design original case data in a few minutes

Basic 3D case of SenStick

Figure 6 3D case data of SenStick

Sensorized

Chopsticks

Figure 7 Eating activity recognition by sensorized chopsticks

kitchen utensils animals stuffed toy a remote control andany kind of things that can be sensing targets In this sectionwe introduce some case studies results utilizing SenStick

41 Eating Behavior Recognition by Sensorized Chopsticks Inmany cases East Asian people eat meals with chopsticksBy analyzing the data obtained from chopsticks there is apossibility that we can estimate the eating habits includinganswers for questions like ldquohow did you eatrdquo and ldquowhatdid you eatrdquo and so forth These data are very useful formaking healthcare applications and leading a healthy lifeThis is one reason why we develop the sensorized chopsticksusing SenStick shown in Figure 7

Research goals of the eating behavior recognition by sen-sorized chopsticks are as follows G1 (behavior) recognizingthe timing and speed and detecting ldquocroprdquo and ldquointakerdquoaction G2 (food type) classifying food content like ricenoodles and so forth from the eating behavior and G3(person) identifying an individual from using chopsticks Inthis paper we show the research results on G1 and G2

We conducted basic experiments to recognize the eatingbehavior (G1 and G2) We used acceleration and gyroscopedata measured by SenStick where the sensing rate is 10HzThe purpose of experiment 1 is to understand the basic

characteristics of sensing data from sensorized chopstickwhen a participant ate chips The purpose of experiment 2 isto identify the food type from the difference in movement ofchopsticks We assumed 4 classes (C1 rice and dish C2 misosoup C3 noodles and C4 shrimp) as food types

Figure 8 shows time series of sensing data for a set ofeating actions in experiment 1 The eating actions are cropaction and intake action In crop action 6-axis motionsensors detect movement (open and close) of the chopsticksIn intake action sensors detect movement of the arm fromthe dish to the mouth Each graph in Figure 8 shows certaincorrelation with both actions

Figure 9 shows a typical signal for each food in exper-iment 2 In this experiment we calculate the recognitionaccuracy of food type classification by using Random Forestclassier ofWeka Tables 5 and 6 show the classification resultsThe average recognition accuracy was 896 If we did nothave a SenStick it would be hard to develop the sensorizedchopsticks that can be used in the actual environment

42 Daily Activity Monitoring by a Sensorized Belt Recentlycorpulent people are rapidly increasing and about 700 mil-lion people belong to the obesity group [18] Nowadays manyoffice workers use computers heavily and their sitting time

10 Journal of Sensors

Step 1 crop action Step 2 intake action

Lower

Upper

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

X

Y

Z

Synthetic

X

Y

Z

Synthetic

X

Y

Z

Synthetic

X

Y

Z

Synthetic

minus250

minus150

minus50

50

150

250

Ang

ular

velo

city

(rad

s)

minus20

minus10

01020

Acce

lera

tion

(ms

2)

minus250

minus150

minus50

50

150

250

Ang

ular

velo

city

(rad

s)

minus20

minus10

01020

Acce

lera

tion

(ms

2)

Open1 Close2 Raise3 Eat4 Drop5

1 2 3 4 5

Figure 8 Time series of sensing data in eating activity

in office increases Keeping incorrect postures during longworking time increases the risk of obesity of office workersTherefore it is necessary tomonitor the daily activity of officeworkers for correcting the bad habits

Motivated by this we have developed a sensorized beltcalled Waiston Belt [8] which aims to monitor a userrsquosdaily activities for maintaining health As products of belt-type wearable devices Welt [19] and Belty [20] are on saleThese provide the function of waist measurement activity

tracking and step counting However API for obtainingsensing data is not provided and it is not suitable for researchpurposes Generally it is hard for ordinary researchers toembed sensors battery BLE and vibrator into a small buckle-size case that can be worn in everyday life Nam et al [21]have proposed a belt-attached device for monitoring postureHowever the device has not reached a sufficient level to beused on a daily basis because of exposed wiring and uncoolappearance However by extending SenStick we succeeded

Journal of Sensors 11

C1

C2 C3

C4

Typical signal of each food

C1 rice amp dish C2 miso soup C3 noodles C4 shrimp

400

200

0

minus200

minus400

Figure 9 Eating activity recognition by sensorized chopsticks

SenStick

Waiston Belt

Figure 10 Waiston Belt based on SenStick

Table 5 Experimental result (food type)

Food type Number ofpieces of data Precision Recall 119865-Measure

C1 rice and dish 3011 0894 0945 0919C2 miso soup 2187 0907 0841 0873C3 noodles 2761 088 0896 0888C4 shrimp 2091 0907 088 0893

0896 0896 0895

to develop a stylish prototype of Waiston Belt shown inFigure 10 quickly Since the battery life of SenStick is 15 hourswith a small battery (60mAh) we can sense continuously inour daily lives As shown in Figure 11 it is possible to estimatevarious usersrsquo contexts (How much did you walk Are youusing the stairs Is posture good at work) by analyzing timeseries of sensing data obtained from the sensorized belt

An important point in this use case is that usersrsquo contextscan be estimated by combining multiple sensing data For

Table 6 Confusion matrix of food type recognition

PredictedC1 C2 C3 C4

ActualC1 rice and dish 094 004 001 000C2 miso soup 012 084 004 000C3 noodles 002 002 090 006C4 shrimp 001 001 011 088

example climbing stairs is estimated from the changes ofacceleration and atmospheric pressure The change of thehumidity data will be helpful to detect that a user enteredan air-conditioned room A SenStick makes it possible toeasily carry out research on behavior recognition with acombination of such multiple sensors

We introduce basic experiments about the posture recog-nition during deskwork by using this beltThe purpose of thisexperiment is to identify the posture type from the attitude

12 Journal of Sensors

Enter the lab

HumidityTemp

Humidity dropping

Pressure dropping

Working

Walking WalkingClimbing stairs Sitting (good posture) Sitting (bad posture)

AccsXAccsYAccsZ

60

40

20

1003

1002

1001

1000

999

25

2

15

1

05

0

minus15

minus1

minus05

Air pressure

Figure 11 Time series of sensing data in daily activity

C1 good posture C2 bad posture 1

C3 bad posture 2 C4 bad posture 3

Figure 12 Time series of sensing data in posture monitoring

angle of the belt calculated from 3 axial acceleration sensorsWe set the sensing rate as 10Hz and collected 6000 samples(10min) for each posture We assumed 4 classes (C1 goodposture C2 bad posture 1 C3 bad posture 2 and C4 badposture 3) as shown in Figure 12

In this experiment we calculated the recognition accu-racy of posture type classification by using Random Forestclassifier of Weka Tables 7 and 8 show the classificationresults The average recognition accuracy was 997 Inthe future based on the result of posture estimation we

Journal of Sensors 13

Sensorized glasses

Figure 13 Sensorized glasses

Table 7 Experimental result (posture type)

Posture type Number ofpieces of data Precision Recall 119865-Measure

C1 good posture 6000 1000 1000 1000C2 bad posture 1 6000 0994 0994 0994C3 bad posture 2 6000 0994 0994 0994C4 bad posture 3 6000 0999 0999 0999

0997 0997 0997

will develop a software application that suggests correctinghisher bad posture to a right posture during desk work

43 Indoor Localization by Sensorized Glasses We have pro-posed an indoor localization system using illuminance-basedtrilateration method [22] In this system we assume that theperson wears glasses having an illuminance sensor as shownin Figure 13 and controllable light sources are deployed in thetarget environment Our system controls lights andmeasuresthe variation of illuminance at the glasses As shown inFigure 14 the distances (1198891ndash1198893) from each light (1198971ndash1198973) to theuser (119906) are estimated from the distance-illumination modelconstructed in advance and userrsquos position is estimated basedon trilateration method using estimated distances

As a result of an experiment where three controllablelight sources were deployed in a room we confirmed thatour system could estimate the position within 05m erroron average By using SenStick we could easily develop thesensorized glasses equipped with illuminance sensor

44 Sensorized Airsoft Gun We have developed the sen-sorized airsoft gun for activity recognition in airsoft gameas shown in Figure 15 Airsoft game is a sport in whichparticipants eliminate opponents by hitting each other usingreplica weapons called airsoft guns The graph of a typicalsignal from the sensorized airsoft gun is shown in Figure 16A user can count the number of shots of a ball by attachingSenStick to his airsoft gun

45 Another Example Studentrsquos Unique Achievements andSurvey Results SenStick is also used in a lecture of IoT

Table 8 Confusion matrix of posture type recognition

PredictedC1 C2 C3 C4

ActualC1 good posture 1000 0000 0000 0000C2 bad posture 1 0000 0994 0006 0001C3 bad posture 2 0000 0006 0994 0000C4 bad posture 3 0000 0001 0000 0999

prototyping Students realize their unique IoT devices easilyby using SenSticks Here we introduce some of the developedIoT devices and survey results about the usefulness ofSenStick answered by students

Figure 17 shows a sensorized pencil with two SenSticksThe sensorized pencil is developed to recognize the charac-ters written by this pencil Even though two SenSticks areembedded to detect precise movement of pencil the size ofthe pencil is reasonable

Figure 18 shows a sensorized name tag anddigital signageSince we changed the battery from 60mAh to 900mAh thetag keeps running more than one week It aims to recordemployeesrsquo or studentsrsquo daily activities to support their healthThe digital signage informs the user who wears a sensorizedname tag about the userrsquos activity data when the user standsin front of it

Figure 19 shows a sensorized spoon used in the digitalfish catching game for children In this game the sensorizedspoon detects the scooping movement of the hand Thuschildren can catch the digital fish displayed by the projectionmapping by using sensorized spoonThis systemwas actuallyused at the universityrsquos open campus event and around100 children enjoyed this game and showed interest in IoTtechnology

The survey results obtained from the ten graduate stu-dents (ninemales and one female) who developed prototypesas mentioned above are shown in Table 9 A questionnaireis a five-grade form (5 strongly agree 4 agree a little 3neither agree nor disagree 2 disagree a little and 1 stronglydisagree) From the results of questions 1 and 2 manyparticipants think that SenStick platform is useful for IoT

14 Journal of Sensors

Position

Distance illumination model

Using trilateration method

a

Light 3

Light 2

Light 1

L = minus421d + 1496

L = minus063d + 501

05 1 15 2 25 3 35 4 450Distance between the illuminance sensor and the lighting device (m)

02468

1012141618

Illum

inan

ce v

alue

(lux

)

Distanceu

d1

d2

d3

l1 p1 x1

l3 p3 x3

l2 p2 x2

Figure 14 Indoor localization by sensorized glasses

Sensorized airsoft gun

Figure 15 Sensorized airsoft gun

research and IoT education Also the results of question 3show that many students are interested in the SenStick andwant to use it again From the results of questions 4 and 5we can see that usability and specification of SenStick arerelatively high There were opinions about the usefulness ofSenStick platform such as ldquoI could quickly try out ideas of IoTprototypingrdquo ldquoIt was easy to collect sensing datardquo and ldquoSinceSenStick is small it was easily attached tomost of the thingsrdquoOn the other hand there were opinions about improvements

such as ldquoI want to use higher sensing frequency (50ndash100Hz)rdquoldquoI want to process a sensing data on SenStickrdquo and ldquoI want tomodify the firmware itself depending on the application ofthe IoT devicerdquo

5 Conclusion

In this paper we have introduced a smart and comprehensivesensing platform called SenStick which is composed of

Journal of Sensors 15

Typical signal of airso gun

Hold a gun Ready

Shoot

1 2 3 4 5 6 70

00

30

60

91

21

51

82

12

42

73

03

33

63

94

24

54

85

15

45

76

06

36

66

97

27

57

88

18

48

79

09

39

69

910

210

510

811

111

411

712

012

312

612

913

2

minus600

minus400

minus200

0

200

400

600

800

acc_xacc_yacc_z

gyro_xgyro_y

acc_synthesis gyro_zgyro_synthesis

Figure 16 Time series of sensing data in shooting an airsoft gun

Sensorized pencil

Figure 17 Sensorized pencil

Sensorized name tagVisualization of sensor data

Figure 18 Sensorized name tag

Sensorized spoon Digital fishing game

Figure 19 Sensorized spoon

Table 9 Questionnaire results about SenStick

Question contents ScoreQ1 Do you think that SenStick will be useful for IoTresearch 49

Q2 Do you think that SenStick will be useful for IoTeducation 47

Q3 Do you want to use SenStick again 49Q4 Do you think that SenStick was easy to use 41Q5 Are you satisfied with specification of SenStick 41

hardware software and 3D case data The main purpose ofour platform is to simplify the sensorization of our personalbelongings

The hardware of SenStick is maybe one of the worldrsquossmallest sensor boards which includes 8 kinds of sensorsand BLE In addition to the hardware we also developedsupport software for managing monitoring recording andviewing the hardware By providing 3D case data all theresearchers can measure every tiny activity in daily life easilyand efficiently For an educational use it is also useful forunderstanding sensors and prototyping the desired systemincluding tiny censored things

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

This work is supported in part by JST PRESTO and JSPSKAKENHI (Grant no 17J10021) and ldquoResearch and Devel-opment of Innovative Network Technologies to Create theFuturerdquo the Commissioned Research of National Instituteof Information and Communications Technology (NICT)Japan

16 Journal of Sensors

References

[1] O Brdiczka M Langet J Maisonnasse and J L CrowleyldquoDetecting human behavior models from multimodal observa-tion in a smart homerdquo IEEE Transactions on Automation Scienceand Engineering vol 6 no 4 pp 588ndash597 2009

[2] J Chen A Kam J Zhang N Liu and L Shue ldquoBathroomactivity monitoring based on soundrdquo in Pervasive ComputingThird International Conference PERVASIVE 2005 MunichGermanyMay 8-13 2005 Proceedings vol 3468 ofLectureNotesin Computer Science pp 47ndash61 Springer Berlin Germany2002

[3] G Bauer K Stockinger and P Lukowicz ldquoRecognizing theUse-Mode of Kitchen Appliances from Their Current Consump-tionrdquo in Smart Sensing and Context vol 5741 of Lecture Notesin Computer Science pp 163ndash176 Springer Berlin HeidelbergBerlin Heidelberg 2009

[4] K Ouchi and M Doi ldquoIndoor-outdoor activity recognition bya smartphonerdquo in Proceedings of the the 2012 ACM Conferencep 537 Pittsburgh Pennsylvania September 2012

[5] A Anjum and M U Ilyas ldquoActivity recognition using smart-phone sensorsrdquo in Proceedings of the IEEE 10th ConsumerCommunications and Networking Conference (CCNC rsquo13) pp914ndash919 IEEE January 2013

[6] O D Lara and M A Labrador ldquoA survey on human activityrecognition using wearable sensorsrdquo IEEE CommunicationsSurveys amp Tutorials vol 15 no 3 pp 1192ndash1209 2013

[7] ldquoJins memerdquo httpsjins-memecom[8] Y Matsuda T Ishioka T Hasegawa et al ldquoWaistonBelt A belt

for monitoring your real abdominal circumference foreverrdquo inProceedings of the ACM International Joint Conference on Perva-sive and Ubiquitous Computing and the 2015 ACM InternationalSymposium on Wearable Computers UbiComp and ISWC 2015pp 329ndash332 September 2015

[9] M Kurahashi K Murao T Terada and M Tsukamoto ldquoPer-sonal identification system based on rotation of toilet paperrollsrdquo in Proceedings of the IEEE International Conference onPervasive Computing and CommunicationsWorkshops (PerComWorkshops rsquo17) pp 521ndash526 IEEE Kona Hawaii USA March2017

[10] ldquoHapiforkrdquo httpswwwhapicom[11] Y Arakawa ldquoSenStick Sensorize every thingsrdquo in Proceedings of

the ACM International Joint Conference on Pervasive and Ubiq-uitous Computing and the 2015 ACM International SymposiumonWearable Computers UbiComp and ISWC 2015 pp 349ndash352September 2015

[12] Y Nakamura T Kanehira Y Arakawa and K YasumotoldquoSenStick 2 Ultra tiny all-in-one sensor with wireless chargingrdquoinProceedings of the 2016ACM International Joint Conference onPervasive and Ubiquitous Computing UbiComp 2016 pp 337ndash340 September 2016

[13] ldquoSensortag cc2541 (texas instruments)rdquo httpwwwticomtoolcc2541dk-sensor

[14] ldquoIot smart modulerdquo httpwwwalpscomjiotsmart[15] ldquoTsnd121 (atr-promotions)rdquo httpwwwatr-pcomproducts

TSND121html[16] ldquoWax9 (axivity)rdquo httpaxivitycomproductwax9[17] Uk biobank ldquoUk biobankrdquo httpwwwukbiobankacuk[18] J C Seidell and J Halberstadt ldquoThe global burden of obesity

and the challenges of preventionrdquo Annals of Nutrition andMetabolism vol 66 supplement 2 pp 7ndash12 2015

[19] ldquoWeltThe smart belt for fashion and healthrdquo httpswwwwelt-corpcom

[20] ldquoBelty power The belt your really deserverdquo httpswwwbeltyco

[21] H Nam J-H Kim and J-I Kim ldquoSmart Belt A wearabledevice for managing abdominal obesityrdquo in Proceedings of theInternational Conference on Big Data and Smart ComputingBigComp 2016 pp 430ndash434 January 2016

[22] K Moriya M Fujimoto Y Arakawa and K Yasumoto ldquoIndoorlocalization based on distance-illuminance model and activecontrol of lighting devicesrdquo in Proceedings of the 2016 Interna-tional Conference on Indoor Positioning and Indoor NavigationIPIN 2016 pp 1ndash6 October 2016

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal of

Volume 201

Submit your manuscripts athttpswwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

Page 8: SenStick: Comprehensive Sensing Platform with an Ultra ...downloads.hindawi.com/journals/js/2017/6308302.pdf · ResearchArticle SenStick: Comprehensive Sensing Platform with an Ultra

8 Journal of Sensors

(i) Frequency(ii) Range

Device select Main view Settings Firmware update

Real-time view Log list in SenStick Download from SenStick

Selected sensorSensor name

Sensing frequency

Range of y-axis

Figure 5 iOS application for SenStick

the sensor When a notification of sensing data isreceived sensorChange event will be issued If Sen-StickprototypestopSensingAndLogging() method is calledSenStick stops sensing and logging

(V) Log Data Reading To read the log data recordedin the flash memory to the application the applicationcalls SenStickprototypenotifySensorLogData()method (Sen-sor represents each sensor name) and enables log datanotification If the application specifies a valid log ID bycalling SenStickprototypewriteSensorLogReadoutTargetID()method SenStick starts informing the sensing data recordedin the log sequentially To receive notification of log datasensorLogDataReceived event will be issued By receiving thisnotification by the application it is possible to acquire the logdata recorded in the memory

(VI) Disconnect After finishing the data acquisition Sen-Stickprototypedisconnect() method will be called to releasethe connection of SenStick And SenStick will begin advertis-ing again

User InterfaceWe explain the user interface of the applicationbased on iOS version Figure 5 shows a sophisticated mobileapplication for SenStick As shown in Figure 5 the userselects target SenStick from devices listThen the applicationshows the main view In the main view the user can selectsensors to be acquired For immediate data monitoring allthe received data via BLE can be shown in the graph view inreal time The application can control all the settings of eachsensor such as sample frequency and sensing range And alsoit has firmware update function Sensing data stored in theflash memory of SenStick is viewed from log list and it can be

downloaded to the smartphone in CSV formatThis functionprevents data loss caused by BLE communication

33 3D Case Data In order to make sensorized things anattachment case for mounting the SenStick is required Wedesigned the basic 3D case of SenStick By extending thisbasic case users can design derivative cases for chopsticksglasses and so on Actually our laboratory has succeeded indesigning cases for several things as shown in Figure 6 Allof these cases data are shared on our community site Eachcase is designed in a few minutes thanks to the basic caseFor example SenStick case for glasses is made by combiningbasic case of SenStick and open 3D data of glasses distributedby i Design Studio In addition due to the popularizationof 3D printers in the world and enhancement of tools andreferences for 3D modeling all the users can print out theirdesired SenStick cases by themselves and attach our sensor toeverything

4 Case Studies of SenStick

SenStick an ultra small sensor can be embedded into varioustiny things in our daily life Our first target is our dailybelongings such as glasses chopsticks and pens becausesensorization of daily belongings is good to intuitively under-standwhat can be sensed andwhich sensor is usefuleffectiveSenStick can upgrade normal glasses to intelligent oneswhichcan monitor a head movement posture and the amount ofsunlight received If SenStick is embedded into chopsticksand pens we can observe eating activities and handwritingactivities respectively In the future we will embed it into astick for elderlymonitoring sports gearsmusical instrument

Journal of Sensors 9

Chopsticks Tooth brush Glasses Umbrella Name card Cookware

A user can design original case data in a few minutes

Basic 3D case of SenStick

Figure 6 3D case data of SenStick

Sensorized

Chopsticks

Figure 7 Eating activity recognition by sensorized chopsticks

kitchen utensils animals stuffed toy a remote control andany kind of things that can be sensing targets In this sectionwe introduce some case studies results utilizing SenStick

41 Eating Behavior Recognition by Sensorized Chopsticks Inmany cases East Asian people eat meals with chopsticksBy analyzing the data obtained from chopsticks there is apossibility that we can estimate the eating habits includinganswers for questions like ldquohow did you eatrdquo and ldquowhatdid you eatrdquo and so forth These data are very useful formaking healthcare applications and leading a healthy lifeThis is one reason why we develop the sensorized chopsticksusing SenStick shown in Figure 7

Research goals of the eating behavior recognition by sen-sorized chopsticks are as follows G1 (behavior) recognizingthe timing and speed and detecting ldquocroprdquo and ldquointakerdquoaction G2 (food type) classifying food content like ricenoodles and so forth from the eating behavior and G3(person) identifying an individual from using chopsticks Inthis paper we show the research results on G1 and G2

We conducted basic experiments to recognize the eatingbehavior (G1 and G2) We used acceleration and gyroscopedata measured by SenStick where the sensing rate is 10HzThe purpose of experiment 1 is to understand the basic

characteristics of sensing data from sensorized chopstickwhen a participant ate chips The purpose of experiment 2 isto identify the food type from the difference in movement ofchopsticks We assumed 4 classes (C1 rice and dish C2 misosoup C3 noodles and C4 shrimp) as food types

Figure 8 shows time series of sensing data for a set ofeating actions in experiment 1 The eating actions are cropaction and intake action In crop action 6-axis motionsensors detect movement (open and close) of the chopsticksIn intake action sensors detect movement of the arm fromthe dish to the mouth Each graph in Figure 8 shows certaincorrelation with both actions

Figure 9 shows a typical signal for each food in exper-iment 2 In this experiment we calculate the recognitionaccuracy of food type classification by using Random Forestclassier ofWeka Tables 5 and 6 show the classification resultsThe average recognition accuracy was 896 If we did nothave a SenStick it would be hard to develop the sensorizedchopsticks that can be used in the actual environment

42 Daily Activity Monitoring by a Sensorized Belt Recentlycorpulent people are rapidly increasing and about 700 mil-lion people belong to the obesity group [18] Nowadays manyoffice workers use computers heavily and their sitting time

10 Journal of Sensors

Step 1 crop action Step 2 intake action

Lower

Upper

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

X

Y

Z

Synthetic

X

Y

Z

Synthetic

X

Y

Z

Synthetic

X

Y

Z

Synthetic

minus250

minus150

minus50

50

150

250

Ang

ular

velo

city

(rad

s)

minus20

minus10

01020

Acce

lera

tion

(ms

2)

minus250

minus150

minus50

50

150

250

Ang

ular

velo

city

(rad

s)

minus20

minus10

01020

Acce

lera

tion

(ms

2)

Open1 Close2 Raise3 Eat4 Drop5

1 2 3 4 5

Figure 8 Time series of sensing data in eating activity

in office increases Keeping incorrect postures during longworking time increases the risk of obesity of office workersTherefore it is necessary tomonitor the daily activity of officeworkers for correcting the bad habits

Motivated by this we have developed a sensorized beltcalled Waiston Belt [8] which aims to monitor a userrsquosdaily activities for maintaining health As products of belt-type wearable devices Welt [19] and Belty [20] are on saleThese provide the function of waist measurement activity

tracking and step counting However API for obtainingsensing data is not provided and it is not suitable for researchpurposes Generally it is hard for ordinary researchers toembed sensors battery BLE and vibrator into a small buckle-size case that can be worn in everyday life Nam et al [21]have proposed a belt-attached device for monitoring postureHowever the device has not reached a sufficient level to beused on a daily basis because of exposed wiring and uncoolappearance However by extending SenStick we succeeded

Journal of Sensors 11

C1

C2 C3

C4

Typical signal of each food

C1 rice amp dish C2 miso soup C3 noodles C4 shrimp

400

200

0

minus200

minus400

Figure 9 Eating activity recognition by sensorized chopsticks

SenStick

Waiston Belt

Figure 10 Waiston Belt based on SenStick

Table 5 Experimental result (food type)

Food type Number ofpieces of data Precision Recall 119865-Measure

C1 rice and dish 3011 0894 0945 0919C2 miso soup 2187 0907 0841 0873C3 noodles 2761 088 0896 0888C4 shrimp 2091 0907 088 0893

0896 0896 0895

to develop a stylish prototype of Waiston Belt shown inFigure 10 quickly Since the battery life of SenStick is 15 hourswith a small battery (60mAh) we can sense continuously inour daily lives As shown in Figure 11 it is possible to estimatevarious usersrsquo contexts (How much did you walk Are youusing the stairs Is posture good at work) by analyzing timeseries of sensing data obtained from the sensorized belt

An important point in this use case is that usersrsquo contextscan be estimated by combining multiple sensing data For

Table 6 Confusion matrix of food type recognition

PredictedC1 C2 C3 C4

ActualC1 rice and dish 094 004 001 000C2 miso soup 012 084 004 000C3 noodles 002 002 090 006C4 shrimp 001 001 011 088

example climbing stairs is estimated from the changes ofacceleration and atmospheric pressure The change of thehumidity data will be helpful to detect that a user enteredan air-conditioned room A SenStick makes it possible toeasily carry out research on behavior recognition with acombination of such multiple sensors

We introduce basic experiments about the posture recog-nition during deskwork by using this beltThe purpose of thisexperiment is to identify the posture type from the attitude

12 Journal of Sensors

Enter the lab

HumidityTemp

Humidity dropping

Pressure dropping

Working

Walking WalkingClimbing stairs Sitting (good posture) Sitting (bad posture)

AccsXAccsYAccsZ

60

40

20

1003

1002

1001

1000

999

25

2

15

1

05

0

minus15

minus1

minus05

Air pressure

Figure 11 Time series of sensing data in daily activity

C1 good posture C2 bad posture 1

C3 bad posture 2 C4 bad posture 3

Figure 12 Time series of sensing data in posture monitoring

angle of the belt calculated from 3 axial acceleration sensorsWe set the sensing rate as 10Hz and collected 6000 samples(10min) for each posture We assumed 4 classes (C1 goodposture C2 bad posture 1 C3 bad posture 2 and C4 badposture 3) as shown in Figure 12

In this experiment we calculated the recognition accu-racy of posture type classification by using Random Forestclassifier of Weka Tables 7 and 8 show the classificationresults The average recognition accuracy was 997 Inthe future based on the result of posture estimation we

Journal of Sensors 13

Sensorized glasses

Figure 13 Sensorized glasses

Table 7 Experimental result (posture type)

Posture type Number ofpieces of data Precision Recall 119865-Measure

C1 good posture 6000 1000 1000 1000C2 bad posture 1 6000 0994 0994 0994C3 bad posture 2 6000 0994 0994 0994C4 bad posture 3 6000 0999 0999 0999

0997 0997 0997

will develop a software application that suggests correctinghisher bad posture to a right posture during desk work

43 Indoor Localization by Sensorized Glasses We have pro-posed an indoor localization system using illuminance-basedtrilateration method [22] In this system we assume that theperson wears glasses having an illuminance sensor as shownin Figure 13 and controllable light sources are deployed in thetarget environment Our system controls lights andmeasuresthe variation of illuminance at the glasses As shown inFigure 14 the distances (1198891ndash1198893) from each light (1198971ndash1198973) to theuser (119906) are estimated from the distance-illumination modelconstructed in advance and userrsquos position is estimated basedon trilateration method using estimated distances

As a result of an experiment where three controllablelight sources were deployed in a room we confirmed thatour system could estimate the position within 05m erroron average By using SenStick we could easily develop thesensorized glasses equipped with illuminance sensor

44 Sensorized Airsoft Gun We have developed the sen-sorized airsoft gun for activity recognition in airsoft gameas shown in Figure 15 Airsoft game is a sport in whichparticipants eliminate opponents by hitting each other usingreplica weapons called airsoft guns The graph of a typicalsignal from the sensorized airsoft gun is shown in Figure 16A user can count the number of shots of a ball by attachingSenStick to his airsoft gun

45 Another Example Studentrsquos Unique Achievements andSurvey Results SenStick is also used in a lecture of IoT

Table 8 Confusion matrix of posture type recognition

PredictedC1 C2 C3 C4

ActualC1 good posture 1000 0000 0000 0000C2 bad posture 1 0000 0994 0006 0001C3 bad posture 2 0000 0006 0994 0000C4 bad posture 3 0000 0001 0000 0999

prototyping Students realize their unique IoT devices easilyby using SenSticks Here we introduce some of the developedIoT devices and survey results about the usefulness ofSenStick answered by students

Figure 17 shows a sensorized pencil with two SenSticksThe sensorized pencil is developed to recognize the charac-ters written by this pencil Even though two SenSticks areembedded to detect precise movement of pencil the size ofthe pencil is reasonable

Figure 18 shows a sensorized name tag anddigital signageSince we changed the battery from 60mAh to 900mAh thetag keeps running more than one week It aims to recordemployeesrsquo or studentsrsquo daily activities to support their healthThe digital signage informs the user who wears a sensorizedname tag about the userrsquos activity data when the user standsin front of it

Figure 19 shows a sensorized spoon used in the digitalfish catching game for children In this game the sensorizedspoon detects the scooping movement of the hand Thuschildren can catch the digital fish displayed by the projectionmapping by using sensorized spoonThis systemwas actuallyused at the universityrsquos open campus event and around100 children enjoyed this game and showed interest in IoTtechnology

The survey results obtained from the ten graduate stu-dents (ninemales and one female) who developed prototypesas mentioned above are shown in Table 9 A questionnaireis a five-grade form (5 strongly agree 4 agree a little 3neither agree nor disagree 2 disagree a little and 1 stronglydisagree) From the results of questions 1 and 2 manyparticipants think that SenStick platform is useful for IoT

14 Journal of Sensors

Position

Distance illumination model

Using trilateration method

a

Light 3

Light 2

Light 1

L = minus421d + 1496

L = minus063d + 501

05 1 15 2 25 3 35 4 450Distance between the illuminance sensor and the lighting device (m)

02468

1012141618

Illum

inan

ce v

alue

(lux

)

Distanceu

d1

d2

d3

l1 p1 x1

l3 p3 x3

l2 p2 x2

Figure 14 Indoor localization by sensorized glasses

Sensorized airsoft gun

Figure 15 Sensorized airsoft gun

research and IoT education Also the results of question 3show that many students are interested in the SenStick andwant to use it again From the results of questions 4 and 5we can see that usability and specification of SenStick arerelatively high There were opinions about the usefulness ofSenStick platform such as ldquoI could quickly try out ideas of IoTprototypingrdquo ldquoIt was easy to collect sensing datardquo and ldquoSinceSenStick is small it was easily attached tomost of the thingsrdquoOn the other hand there were opinions about improvements

such as ldquoI want to use higher sensing frequency (50ndash100Hz)rdquoldquoI want to process a sensing data on SenStickrdquo and ldquoI want tomodify the firmware itself depending on the application ofthe IoT devicerdquo

5 Conclusion

In this paper we have introduced a smart and comprehensivesensing platform called SenStick which is composed of

Journal of Sensors 15

Typical signal of airso gun

Hold a gun Ready

Shoot

1 2 3 4 5 6 70

00

30

60

91

21

51

82

12

42

73

03

33

63

94

24

54

85

15

45

76

06

36

66

97

27

57

88

18

48

79

09

39

69

910

210

510

811

111

411

712

012

312

612

913

2

minus600

minus400

minus200

0

200

400

600

800

acc_xacc_yacc_z

gyro_xgyro_y

acc_synthesis gyro_zgyro_synthesis

Figure 16 Time series of sensing data in shooting an airsoft gun

Sensorized pencil

Figure 17 Sensorized pencil

Sensorized name tagVisualization of sensor data

Figure 18 Sensorized name tag

Sensorized spoon Digital fishing game

Figure 19 Sensorized spoon

Table 9 Questionnaire results about SenStick

Question contents ScoreQ1 Do you think that SenStick will be useful for IoTresearch 49

Q2 Do you think that SenStick will be useful for IoTeducation 47

Q3 Do you want to use SenStick again 49Q4 Do you think that SenStick was easy to use 41Q5 Are you satisfied with specification of SenStick 41

hardware software and 3D case data The main purpose ofour platform is to simplify the sensorization of our personalbelongings

The hardware of SenStick is maybe one of the worldrsquossmallest sensor boards which includes 8 kinds of sensorsand BLE In addition to the hardware we also developedsupport software for managing monitoring recording andviewing the hardware By providing 3D case data all theresearchers can measure every tiny activity in daily life easilyand efficiently For an educational use it is also useful forunderstanding sensors and prototyping the desired systemincluding tiny censored things

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

This work is supported in part by JST PRESTO and JSPSKAKENHI (Grant no 17J10021) and ldquoResearch and Devel-opment of Innovative Network Technologies to Create theFuturerdquo the Commissioned Research of National Instituteof Information and Communications Technology (NICT)Japan

16 Journal of Sensors

References

[1] O Brdiczka M Langet J Maisonnasse and J L CrowleyldquoDetecting human behavior models from multimodal observa-tion in a smart homerdquo IEEE Transactions on Automation Scienceand Engineering vol 6 no 4 pp 588ndash597 2009

[2] J Chen A Kam J Zhang N Liu and L Shue ldquoBathroomactivity monitoring based on soundrdquo in Pervasive ComputingThird International Conference PERVASIVE 2005 MunichGermanyMay 8-13 2005 Proceedings vol 3468 ofLectureNotesin Computer Science pp 47ndash61 Springer Berlin Germany2002

[3] G Bauer K Stockinger and P Lukowicz ldquoRecognizing theUse-Mode of Kitchen Appliances from Their Current Consump-tionrdquo in Smart Sensing and Context vol 5741 of Lecture Notesin Computer Science pp 163ndash176 Springer Berlin HeidelbergBerlin Heidelberg 2009

[4] K Ouchi and M Doi ldquoIndoor-outdoor activity recognition bya smartphonerdquo in Proceedings of the the 2012 ACM Conferencep 537 Pittsburgh Pennsylvania September 2012

[5] A Anjum and M U Ilyas ldquoActivity recognition using smart-phone sensorsrdquo in Proceedings of the IEEE 10th ConsumerCommunications and Networking Conference (CCNC rsquo13) pp914ndash919 IEEE January 2013

[6] O D Lara and M A Labrador ldquoA survey on human activityrecognition using wearable sensorsrdquo IEEE CommunicationsSurveys amp Tutorials vol 15 no 3 pp 1192ndash1209 2013

[7] ldquoJins memerdquo httpsjins-memecom[8] Y Matsuda T Ishioka T Hasegawa et al ldquoWaistonBelt A belt

for monitoring your real abdominal circumference foreverrdquo inProceedings of the ACM International Joint Conference on Perva-sive and Ubiquitous Computing and the 2015 ACM InternationalSymposium on Wearable Computers UbiComp and ISWC 2015pp 329ndash332 September 2015

[9] M Kurahashi K Murao T Terada and M Tsukamoto ldquoPer-sonal identification system based on rotation of toilet paperrollsrdquo in Proceedings of the IEEE International Conference onPervasive Computing and CommunicationsWorkshops (PerComWorkshops rsquo17) pp 521ndash526 IEEE Kona Hawaii USA March2017

[10] ldquoHapiforkrdquo httpswwwhapicom[11] Y Arakawa ldquoSenStick Sensorize every thingsrdquo in Proceedings of

the ACM International Joint Conference on Pervasive and Ubiq-uitous Computing and the 2015 ACM International SymposiumonWearable Computers UbiComp and ISWC 2015 pp 349ndash352September 2015

[12] Y Nakamura T Kanehira Y Arakawa and K YasumotoldquoSenStick 2 Ultra tiny all-in-one sensor with wireless chargingrdquoinProceedings of the 2016ACM International Joint Conference onPervasive and Ubiquitous Computing UbiComp 2016 pp 337ndash340 September 2016

[13] ldquoSensortag cc2541 (texas instruments)rdquo httpwwwticomtoolcc2541dk-sensor

[14] ldquoIot smart modulerdquo httpwwwalpscomjiotsmart[15] ldquoTsnd121 (atr-promotions)rdquo httpwwwatr-pcomproducts

TSND121html[16] ldquoWax9 (axivity)rdquo httpaxivitycomproductwax9[17] Uk biobank ldquoUk biobankrdquo httpwwwukbiobankacuk[18] J C Seidell and J Halberstadt ldquoThe global burden of obesity

and the challenges of preventionrdquo Annals of Nutrition andMetabolism vol 66 supplement 2 pp 7ndash12 2015

[19] ldquoWeltThe smart belt for fashion and healthrdquo httpswwwwelt-corpcom

[20] ldquoBelty power The belt your really deserverdquo httpswwwbeltyco

[21] H Nam J-H Kim and J-I Kim ldquoSmart Belt A wearabledevice for managing abdominal obesityrdquo in Proceedings of theInternational Conference on Big Data and Smart ComputingBigComp 2016 pp 430ndash434 January 2016

[22] K Moriya M Fujimoto Y Arakawa and K Yasumoto ldquoIndoorlocalization based on distance-illuminance model and activecontrol of lighting devicesrdquo in Proceedings of the 2016 Interna-tional Conference on Indoor Positioning and Indoor NavigationIPIN 2016 pp 1ndash6 October 2016

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal of

Volume 201

Submit your manuscripts athttpswwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

Page 9: SenStick: Comprehensive Sensing Platform with an Ultra ...downloads.hindawi.com/journals/js/2017/6308302.pdf · ResearchArticle SenStick: Comprehensive Sensing Platform with an Ultra

Journal of Sensors 9

Chopsticks Tooth brush Glasses Umbrella Name card Cookware

A user can design original case data in a few minutes

Basic 3D case of SenStick

Figure 6 3D case data of SenStick

Sensorized

Chopsticks

Figure 7 Eating activity recognition by sensorized chopsticks

kitchen utensils animals stuffed toy a remote control andany kind of things that can be sensing targets In this sectionwe introduce some case studies results utilizing SenStick

41 Eating Behavior Recognition by Sensorized Chopsticks Inmany cases East Asian people eat meals with chopsticksBy analyzing the data obtained from chopsticks there is apossibility that we can estimate the eating habits includinganswers for questions like ldquohow did you eatrdquo and ldquowhatdid you eatrdquo and so forth These data are very useful formaking healthcare applications and leading a healthy lifeThis is one reason why we develop the sensorized chopsticksusing SenStick shown in Figure 7

Research goals of the eating behavior recognition by sen-sorized chopsticks are as follows G1 (behavior) recognizingthe timing and speed and detecting ldquocroprdquo and ldquointakerdquoaction G2 (food type) classifying food content like ricenoodles and so forth from the eating behavior and G3(person) identifying an individual from using chopsticks Inthis paper we show the research results on G1 and G2

We conducted basic experiments to recognize the eatingbehavior (G1 and G2) We used acceleration and gyroscopedata measured by SenStick where the sensing rate is 10HzThe purpose of experiment 1 is to understand the basic

characteristics of sensing data from sensorized chopstickwhen a participant ate chips The purpose of experiment 2 isto identify the food type from the difference in movement ofchopsticks We assumed 4 classes (C1 rice and dish C2 misosoup C3 noodles and C4 shrimp) as food types

Figure 8 shows time series of sensing data for a set ofeating actions in experiment 1 The eating actions are cropaction and intake action In crop action 6-axis motionsensors detect movement (open and close) of the chopsticksIn intake action sensors detect movement of the arm fromthe dish to the mouth Each graph in Figure 8 shows certaincorrelation with both actions

Figure 9 shows a typical signal for each food in exper-iment 2 In this experiment we calculate the recognitionaccuracy of food type classification by using Random Forestclassier ofWeka Tables 5 and 6 show the classification resultsThe average recognition accuracy was 896 If we did nothave a SenStick it would be hard to develop the sensorizedchopsticks that can be used in the actual environment

42 Daily Activity Monitoring by a Sensorized Belt Recentlycorpulent people are rapidly increasing and about 700 mil-lion people belong to the obesity group [18] Nowadays manyoffice workers use computers heavily and their sitting time

10 Journal of Sensors

Step 1 crop action Step 2 intake action

Lower

Upper

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

X

Y

Z

Synthetic

X

Y

Z

Synthetic

X

Y

Z

Synthetic

X

Y

Z

Synthetic

minus250

minus150

minus50

50

150

250

Ang

ular

velo

city

(rad

s)

minus20

minus10

01020

Acce

lera

tion

(ms

2)

minus250

minus150

minus50

50

150

250

Ang

ular

velo

city

(rad

s)

minus20

minus10

01020

Acce

lera

tion

(ms

2)

Open1 Close2 Raise3 Eat4 Drop5

1 2 3 4 5

Figure 8 Time series of sensing data in eating activity

in office increases Keeping incorrect postures during longworking time increases the risk of obesity of office workersTherefore it is necessary tomonitor the daily activity of officeworkers for correcting the bad habits

Motivated by this we have developed a sensorized beltcalled Waiston Belt [8] which aims to monitor a userrsquosdaily activities for maintaining health As products of belt-type wearable devices Welt [19] and Belty [20] are on saleThese provide the function of waist measurement activity

tracking and step counting However API for obtainingsensing data is not provided and it is not suitable for researchpurposes Generally it is hard for ordinary researchers toembed sensors battery BLE and vibrator into a small buckle-size case that can be worn in everyday life Nam et al [21]have proposed a belt-attached device for monitoring postureHowever the device has not reached a sufficient level to beused on a daily basis because of exposed wiring and uncoolappearance However by extending SenStick we succeeded

Journal of Sensors 11

C1

C2 C3

C4

Typical signal of each food

C1 rice amp dish C2 miso soup C3 noodles C4 shrimp

400

200

0

minus200

minus400

Figure 9 Eating activity recognition by sensorized chopsticks

SenStick

Waiston Belt

Figure 10 Waiston Belt based on SenStick

Table 5 Experimental result (food type)

Food type Number ofpieces of data Precision Recall 119865-Measure

C1 rice and dish 3011 0894 0945 0919C2 miso soup 2187 0907 0841 0873C3 noodles 2761 088 0896 0888C4 shrimp 2091 0907 088 0893

0896 0896 0895

to develop a stylish prototype of Waiston Belt shown inFigure 10 quickly Since the battery life of SenStick is 15 hourswith a small battery (60mAh) we can sense continuously inour daily lives As shown in Figure 11 it is possible to estimatevarious usersrsquo contexts (How much did you walk Are youusing the stairs Is posture good at work) by analyzing timeseries of sensing data obtained from the sensorized belt

An important point in this use case is that usersrsquo contextscan be estimated by combining multiple sensing data For

Table 6 Confusion matrix of food type recognition

PredictedC1 C2 C3 C4

ActualC1 rice and dish 094 004 001 000C2 miso soup 012 084 004 000C3 noodles 002 002 090 006C4 shrimp 001 001 011 088

example climbing stairs is estimated from the changes ofacceleration and atmospheric pressure The change of thehumidity data will be helpful to detect that a user enteredan air-conditioned room A SenStick makes it possible toeasily carry out research on behavior recognition with acombination of such multiple sensors

We introduce basic experiments about the posture recog-nition during deskwork by using this beltThe purpose of thisexperiment is to identify the posture type from the attitude

12 Journal of Sensors

Enter the lab

HumidityTemp

Humidity dropping

Pressure dropping

Working

Walking WalkingClimbing stairs Sitting (good posture) Sitting (bad posture)

AccsXAccsYAccsZ

60

40

20

1003

1002

1001

1000

999

25

2

15

1

05

0

minus15

minus1

minus05

Air pressure

Figure 11 Time series of sensing data in daily activity

C1 good posture C2 bad posture 1

C3 bad posture 2 C4 bad posture 3

Figure 12 Time series of sensing data in posture monitoring

angle of the belt calculated from 3 axial acceleration sensorsWe set the sensing rate as 10Hz and collected 6000 samples(10min) for each posture We assumed 4 classes (C1 goodposture C2 bad posture 1 C3 bad posture 2 and C4 badposture 3) as shown in Figure 12

In this experiment we calculated the recognition accu-racy of posture type classification by using Random Forestclassifier of Weka Tables 7 and 8 show the classificationresults The average recognition accuracy was 997 Inthe future based on the result of posture estimation we

Journal of Sensors 13

Sensorized glasses

Figure 13 Sensorized glasses

Table 7 Experimental result (posture type)

Posture type Number ofpieces of data Precision Recall 119865-Measure

C1 good posture 6000 1000 1000 1000C2 bad posture 1 6000 0994 0994 0994C3 bad posture 2 6000 0994 0994 0994C4 bad posture 3 6000 0999 0999 0999

0997 0997 0997

will develop a software application that suggests correctinghisher bad posture to a right posture during desk work

43 Indoor Localization by Sensorized Glasses We have pro-posed an indoor localization system using illuminance-basedtrilateration method [22] In this system we assume that theperson wears glasses having an illuminance sensor as shownin Figure 13 and controllable light sources are deployed in thetarget environment Our system controls lights andmeasuresthe variation of illuminance at the glasses As shown inFigure 14 the distances (1198891ndash1198893) from each light (1198971ndash1198973) to theuser (119906) are estimated from the distance-illumination modelconstructed in advance and userrsquos position is estimated basedon trilateration method using estimated distances

As a result of an experiment where three controllablelight sources were deployed in a room we confirmed thatour system could estimate the position within 05m erroron average By using SenStick we could easily develop thesensorized glasses equipped with illuminance sensor

44 Sensorized Airsoft Gun We have developed the sen-sorized airsoft gun for activity recognition in airsoft gameas shown in Figure 15 Airsoft game is a sport in whichparticipants eliminate opponents by hitting each other usingreplica weapons called airsoft guns The graph of a typicalsignal from the sensorized airsoft gun is shown in Figure 16A user can count the number of shots of a ball by attachingSenStick to his airsoft gun

45 Another Example Studentrsquos Unique Achievements andSurvey Results SenStick is also used in a lecture of IoT

Table 8 Confusion matrix of posture type recognition

PredictedC1 C2 C3 C4

ActualC1 good posture 1000 0000 0000 0000C2 bad posture 1 0000 0994 0006 0001C3 bad posture 2 0000 0006 0994 0000C4 bad posture 3 0000 0001 0000 0999

prototyping Students realize their unique IoT devices easilyby using SenSticks Here we introduce some of the developedIoT devices and survey results about the usefulness ofSenStick answered by students

Figure 17 shows a sensorized pencil with two SenSticksThe sensorized pencil is developed to recognize the charac-ters written by this pencil Even though two SenSticks areembedded to detect precise movement of pencil the size ofthe pencil is reasonable

Figure 18 shows a sensorized name tag anddigital signageSince we changed the battery from 60mAh to 900mAh thetag keeps running more than one week It aims to recordemployeesrsquo or studentsrsquo daily activities to support their healthThe digital signage informs the user who wears a sensorizedname tag about the userrsquos activity data when the user standsin front of it

Figure 19 shows a sensorized spoon used in the digitalfish catching game for children In this game the sensorizedspoon detects the scooping movement of the hand Thuschildren can catch the digital fish displayed by the projectionmapping by using sensorized spoonThis systemwas actuallyused at the universityrsquos open campus event and around100 children enjoyed this game and showed interest in IoTtechnology

The survey results obtained from the ten graduate stu-dents (ninemales and one female) who developed prototypesas mentioned above are shown in Table 9 A questionnaireis a five-grade form (5 strongly agree 4 agree a little 3neither agree nor disagree 2 disagree a little and 1 stronglydisagree) From the results of questions 1 and 2 manyparticipants think that SenStick platform is useful for IoT

14 Journal of Sensors

Position

Distance illumination model

Using trilateration method

a

Light 3

Light 2

Light 1

L = minus421d + 1496

L = minus063d + 501

05 1 15 2 25 3 35 4 450Distance between the illuminance sensor and the lighting device (m)

02468

1012141618

Illum

inan

ce v

alue

(lux

)

Distanceu

d1

d2

d3

l1 p1 x1

l3 p3 x3

l2 p2 x2

Figure 14 Indoor localization by sensorized glasses

Sensorized airsoft gun

Figure 15 Sensorized airsoft gun

research and IoT education Also the results of question 3show that many students are interested in the SenStick andwant to use it again From the results of questions 4 and 5we can see that usability and specification of SenStick arerelatively high There were opinions about the usefulness ofSenStick platform such as ldquoI could quickly try out ideas of IoTprototypingrdquo ldquoIt was easy to collect sensing datardquo and ldquoSinceSenStick is small it was easily attached tomost of the thingsrdquoOn the other hand there were opinions about improvements

such as ldquoI want to use higher sensing frequency (50ndash100Hz)rdquoldquoI want to process a sensing data on SenStickrdquo and ldquoI want tomodify the firmware itself depending on the application ofthe IoT devicerdquo

5 Conclusion

In this paper we have introduced a smart and comprehensivesensing platform called SenStick which is composed of

Journal of Sensors 15

Typical signal of airso gun

Hold a gun Ready

Shoot

1 2 3 4 5 6 70

00

30

60

91

21

51

82

12

42

73

03

33

63

94

24

54

85

15

45

76

06

36

66

97

27

57

88

18

48

79

09

39

69

910

210

510

811

111

411

712

012

312

612

913

2

minus600

minus400

minus200

0

200

400

600

800

acc_xacc_yacc_z

gyro_xgyro_y

acc_synthesis gyro_zgyro_synthesis

Figure 16 Time series of sensing data in shooting an airsoft gun

Sensorized pencil

Figure 17 Sensorized pencil

Sensorized name tagVisualization of sensor data

Figure 18 Sensorized name tag

Sensorized spoon Digital fishing game

Figure 19 Sensorized spoon

Table 9 Questionnaire results about SenStick

Question contents ScoreQ1 Do you think that SenStick will be useful for IoTresearch 49

Q2 Do you think that SenStick will be useful for IoTeducation 47

Q3 Do you want to use SenStick again 49Q4 Do you think that SenStick was easy to use 41Q5 Are you satisfied with specification of SenStick 41

hardware software and 3D case data The main purpose ofour platform is to simplify the sensorization of our personalbelongings

The hardware of SenStick is maybe one of the worldrsquossmallest sensor boards which includes 8 kinds of sensorsand BLE In addition to the hardware we also developedsupport software for managing monitoring recording andviewing the hardware By providing 3D case data all theresearchers can measure every tiny activity in daily life easilyand efficiently For an educational use it is also useful forunderstanding sensors and prototyping the desired systemincluding tiny censored things

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

This work is supported in part by JST PRESTO and JSPSKAKENHI (Grant no 17J10021) and ldquoResearch and Devel-opment of Innovative Network Technologies to Create theFuturerdquo the Commissioned Research of National Instituteof Information and Communications Technology (NICT)Japan

16 Journal of Sensors

References

[1] O Brdiczka M Langet J Maisonnasse and J L CrowleyldquoDetecting human behavior models from multimodal observa-tion in a smart homerdquo IEEE Transactions on Automation Scienceand Engineering vol 6 no 4 pp 588ndash597 2009

[2] J Chen A Kam J Zhang N Liu and L Shue ldquoBathroomactivity monitoring based on soundrdquo in Pervasive ComputingThird International Conference PERVASIVE 2005 MunichGermanyMay 8-13 2005 Proceedings vol 3468 ofLectureNotesin Computer Science pp 47ndash61 Springer Berlin Germany2002

[3] G Bauer K Stockinger and P Lukowicz ldquoRecognizing theUse-Mode of Kitchen Appliances from Their Current Consump-tionrdquo in Smart Sensing and Context vol 5741 of Lecture Notesin Computer Science pp 163ndash176 Springer Berlin HeidelbergBerlin Heidelberg 2009

[4] K Ouchi and M Doi ldquoIndoor-outdoor activity recognition bya smartphonerdquo in Proceedings of the the 2012 ACM Conferencep 537 Pittsburgh Pennsylvania September 2012

[5] A Anjum and M U Ilyas ldquoActivity recognition using smart-phone sensorsrdquo in Proceedings of the IEEE 10th ConsumerCommunications and Networking Conference (CCNC rsquo13) pp914ndash919 IEEE January 2013

[6] O D Lara and M A Labrador ldquoA survey on human activityrecognition using wearable sensorsrdquo IEEE CommunicationsSurveys amp Tutorials vol 15 no 3 pp 1192ndash1209 2013

[7] ldquoJins memerdquo httpsjins-memecom[8] Y Matsuda T Ishioka T Hasegawa et al ldquoWaistonBelt A belt

for monitoring your real abdominal circumference foreverrdquo inProceedings of the ACM International Joint Conference on Perva-sive and Ubiquitous Computing and the 2015 ACM InternationalSymposium on Wearable Computers UbiComp and ISWC 2015pp 329ndash332 September 2015

[9] M Kurahashi K Murao T Terada and M Tsukamoto ldquoPer-sonal identification system based on rotation of toilet paperrollsrdquo in Proceedings of the IEEE International Conference onPervasive Computing and CommunicationsWorkshops (PerComWorkshops rsquo17) pp 521ndash526 IEEE Kona Hawaii USA March2017

[10] ldquoHapiforkrdquo httpswwwhapicom[11] Y Arakawa ldquoSenStick Sensorize every thingsrdquo in Proceedings of

the ACM International Joint Conference on Pervasive and Ubiq-uitous Computing and the 2015 ACM International SymposiumonWearable Computers UbiComp and ISWC 2015 pp 349ndash352September 2015

[12] Y Nakamura T Kanehira Y Arakawa and K YasumotoldquoSenStick 2 Ultra tiny all-in-one sensor with wireless chargingrdquoinProceedings of the 2016ACM International Joint Conference onPervasive and Ubiquitous Computing UbiComp 2016 pp 337ndash340 September 2016

[13] ldquoSensortag cc2541 (texas instruments)rdquo httpwwwticomtoolcc2541dk-sensor

[14] ldquoIot smart modulerdquo httpwwwalpscomjiotsmart[15] ldquoTsnd121 (atr-promotions)rdquo httpwwwatr-pcomproducts

TSND121html[16] ldquoWax9 (axivity)rdquo httpaxivitycomproductwax9[17] Uk biobank ldquoUk biobankrdquo httpwwwukbiobankacuk[18] J C Seidell and J Halberstadt ldquoThe global burden of obesity

and the challenges of preventionrdquo Annals of Nutrition andMetabolism vol 66 supplement 2 pp 7ndash12 2015

[19] ldquoWeltThe smart belt for fashion and healthrdquo httpswwwwelt-corpcom

[20] ldquoBelty power The belt your really deserverdquo httpswwwbeltyco

[21] H Nam J-H Kim and J-I Kim ldquoSmart Belt A wearabledevice for managing abdominal obesityrdquo in Proceedings of theInternational Conference on Big Data and Smart ComputingBigComp 2016 pp 430ndash434 January 2016

[22] K Moriya M Fujimoto Y Arakawa and K Yasumoto ldquoIndoorlocalization based on distance-illuminance model and activecontrol of lighting devicesrdquo in Proceedings of the 2016 Interna-tional Conference on Indoor Positioning and Indoor NavigationIPIN 2016 pp 1ndash6 October 2016

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal of

Volume 201

Submit your manuscripts athttpswwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

Page 10: SenStick: Comprehensive Sensing Platform with an Ultra ...downloads.hindawi.com/journals/js/2017/6308302.pdf · ResearchArticle SenStick: Comprehensive Sensing Platform with an Ultra

10 Journal of Sensors

Step 1 crop action Step 2 intake action

Lower

Upper

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

2 4 6 8 10 12 140Elapsed time (sec)

X

Y

Z

Synthetic

X

Y

Z

Synthetic

X

Y

Z

Synthetic

X

Y

Z

Synthetic

minus250

minus150

minus50

50

150

250

Ang

ular

velo

city

(rad

s)

minus20

minus10

01020

Acce

lera

tion

(ms

2)

minus250

minus150

minus50

50

150

250

Ang

ular

velo

city

(rad

s)

minus20

minus10

01020

Acce

lera

tion

(ms

2)

Open1 Close2 Raise3 Eat4 Drop5

1 2 3 4 5

Figure 8 Time series of sensing data in eating activity

in office increases Keeping incorrect postures during longworking time increases the risk of obesity of office workersTherefore it is necessary tomonitor the daily activity of officeworkers for correcting the bad habits

Motivated by this we have developed a sensorized beltcalled Waiston Belt [8] which aims to monitor a userrsquosdaily activities for maintaining health As products of belt-type wearable devices Welt [19] and Belty [20] are on saleThese provide the function of waist measurement activity

tracking and step counting However API for obtainingsensing data is not provided and it is not suitable for researchpurposes Generally it is hard for ordinary researchers toembed sensors battery BLE and vibrator into a small buckle-size case that can be worn in everyday life Nam et al [21]have proposed a belt-attached device for monitoring postureHowever the device has not reached a sufficient level to beused on a daily basis because of exposed wiring and uncoolappearance However by extending SenStick we succeeded

Journal of Sensors 11

C1

C2 C3

C4

Typical signal of each food

C1 rice amp dish C2 miso soup C3 noodles C4 shrimp

400

200

0

minus200

minus400

Figure 9 Eating activity recognition by sensorized chopsticks

SenStick

Waiston Belt

Figure 10 Waiston Belt based on SenStick

Table 5 Experimental result (food type)

Food type Number ofpieces of data Precision Recall 119865-Measure

C1 rice and dish 3011 0894 0945 0919C2 miso soup 2187 0907 0841 0873C3 noodles 2761 088 0896 0888C4 shrimp 2091 0907 088 0893

0896 0896 0895

to develop a stylish prototype of Waiston Belt shown inFigure 10 quickly Since the battery life of SenStick is 15 hourswith a small battery (60mAh) we can sense continuously inour daily lives As shown in Figure 11 it is possible to estimatevarious usersrsquo contexts (How much did you walk Are youusing the stairs Is posture good at work) by analyzing timeseries of sensing data obtained from the sensorized belt

An important point in this use case is that usersrsquo contextscan be estimated by combining multiple sensing data For

Table 6 Confusion matrix of food type recognition

PredictedC1 C2 C3 C4

ActualC1 rice and dish 094 004 001 000C2 miso soup 012 084 004 000C3 noodles 002 002 090 006C4 shrimp 001 001 011 088

example climbing stairs is estimated from the changes ofacceleration and atmospheric pressure The change of thehumidity data will be helpful to detect that a user enteredan air-conditioned room A SenStick makes it possible toeasily carry out research on behavior recognition with acombination of such multiple sensors

We introduce basic experiments about the posture recog-nition during deskwork by using this beltThe purpose of thisexperiment is to identify the posture type from the attitude

12 Journal of Sensors

Enter the lab

HumidityTemp

Humidity dropping

Pressure dropping

Working

Walking WalkingClimbing stairs Sitting (good posture) Sitting (bad posture)

AccsXAccsYAccsZ

60

40

20

1003

1002

1001

1000

999

25

2

15

1

05

0

minus15

minus1

minus05

Air pressure

Figure 11 Time series of sensing data in daily activity

C1 good posture C2 bad posture 1

C3 bad posture 2 C4 bad posture 3

Figure 12 Time series of sensing data in posture monitoring

angle of the belt calculated from 3 axial acceleration sensorsWe set the sensing rate as 10Hz and collected 6000 samples(10min) for each posture We assumed 4 classes (C1 goodposture C2 bad posture 1 C3 bad posture 2 and C4 badposture 3) as shown in Figure 12

In this experiment we calculated the recognition accu-racy of posture type classification by using Random Forestclassifier of Weka Tables 7 and 8 show the classificationresults The average recognition accuracy was 997 Inthe future based on the result of posture estimation we

Journal of Sensors 13

Sensorized glasses

Figure 13 Sensorized glasses

Table 7 Experimental result (posture type)

Posture type Number ofpieces of data Precision Recall 119865-Measure

C1 good posture 6000 1000 1000 1000C2 bad posture 1 6000 0994 0994 0994C3 bad posture 2 6000 0994 0994 0994C4 bad posture 3 6000 0999 0999 0999

0997 0997 0997

will develop a software application that suggests correctinghisher bad posture to a right posture during desk work

43 Indoor Localization by Sensorized Glasses We have pro-posed an indoor localization system using illuminance-basedtrilateration method [22] In this system we assume that theperson wears glasses having an illuminance sensor as shownin Figure 13 and controllable light sources are deployed in thetarget environment Our system controls lights andmeasuresthe variation of illuminance at the glasses As shown inFigure 14 the distances (1198891ndash1198893) from each light (1198971ndash1198973) to theuser (119906) are estimated from the distance-illumination modelconstructed in advance and userrsquos position is estimated basedon trilateration method using estimated distances

As a result of an experiment where three controllablelight sources were deployed in a room we confirmed thatour system could estimate the position within 05m erroron average By using SenStick we could easily develop thesensorized glasses equipped with illuminance sensor

44 Sensorized Airsoft Gun We have developed the sen-sorized airsoft gun for activity recognition in airsoft gameas shown in Figure 15 Airsoft game is a sport in whichparticipants eliminate opponents by hitting each other usingreplica weapons called airsoft guns The graph of a typicalsignal from the sensorized airsoft gun is shown in Figure 16A user can count the number of shots of a ball by attachingSenStick to his airsoft gun

45 Another Example Studentrsquos Unique Achievements andSurvey Results SenStick is also used in a lecture of IoT

Table 8 Confusion matrix of posture type recognition

PredictedC1 C2 C3 C4

ActualC1 good posture 1000 0000 0000 0000C2 bad posture 1 0000 0994 0006 0001C3 bad posture 2 0000 0006 0994 0000C4 bad posture 3 0000 0001 0000 0999

prototyping Students realize their unique IoT devices easilyby using SenSticks Here we introduce some of the developedIoT devices and survey results about the usefulness ofSenStick answered by students

Figure 17 shows a sensorized pencil with two SenSticksThe sensorized pencil is developed to recognize the charac-ters written by this pencil Even though two SenSticks areembedded to detect precise movement of pencil the size ofthe pencil is reasonable

Figure 18 shows a sensorized name tag anddigital signageSince we changed the battery from 60mAh to 900mAh thetag keeps running more than one week It aims to recordemployeesrsquo or studentsrsquo daily activities to support their healthThe digital signage informs the user who wears a sensorizedname tag about the userrsquos activity data when the user standsin front of it

Figure 19 shows a sensorized spoon used in the digitalfish catching game for children In this game the sensorizedspoon detects the scooping movement of the hand Thuschildren can catch the digital fish displayed by the projectionmapping by using sensorized spoonThis systemwas actuallyused at the universityrsquos open campus event and around100 children enjoyed this game and showed interest in IoTtechnology

The survey results obtained from the ten graduate stu-dents (ninemales and one female) who developed prototypesas mentioned above are shown in Table 9 A questionnaireis a five-grade form (5 strongly agree 4 agree a little 3neither agree nor disagree 2 disagree a little and 1 stronglydisagree) From the results of questions 1 and 2 manyparticipants think that SenStick platform is useful for IoT

14 Journal of Sensors

Position

Distance illumination model

Using trilateration method

a

Light 3

Light 2

Light 1

L = minus421d + 1496

L = minus063d + 501

05 1 15 2 25 3 35 4 450Distance between the illuminance sensor and the lighting device (m)

02468

1012141618

Illum

inan

ce v

alue

(lux

)

Distanceu

d1

d2

d3

l1 p1 x1

l3 p3 x3

l2 p2 x2

Figure 14 Indoor localization by sensorized glasses

Sensorized airsoft gun

Figure 15 Sensorized airsoft gun

research and IoT education Also the results of question 3show that many students are interested in the SenStick andwant to use it again From the results of questions 4 and 5we can see that usability and specification of SenStick arerelatively high There were opinions about the usefulness ofSenStick platform such as ldquoI could quickly try out ideas of IoTprototypingrdquo ldquoIt was easy to collect sensing datardquo and ldquoSinceSenStick is small it was easily attached tomost of the thingsrdquoOn the other hand there were opinions about improvements

such as ldquoI want to use higher sensing frequency (50ndash100Hz)rdquoldquoI want to process a sensing data on SenStickrdquo and ldquoI want tomodify the firmware itself depending on the application ofthe IoT devicerdquo

5 Conclusion

In this paper we have introduced a smart and comprehensivesensing platform called SenStick which is composed of

Journal of Sensors 15

Typical signal of airso gun

Hold a gun Ready

Shoot

1 2 3 4 5 6 70

00

30

60

91

21

51

82

12

42

73

03

33

63

94

24

54

85

15

45

76

06

36

66

97

27

57

88

18

48

79

09

39

69

910

210

510

811

111

411

712

012

312

612

913

2

minus600

minus400

minus200

0

200

400

600

800

acc_xacc_yacc_z

gyro_xgyro_y

acc_synthesis gyro_zgyro_synthesis

Figure 16 Time series of sensing data in shooting an airsoft gun

Sensorized pencil

Figure 17 Sensorized pencil

Sensorized name tagVisualization of sensor data

Figure 18 Sensorized name tag

Sensorized spoon Digital fishing game

Figure 19 Sensorized spoon

Table 9 Questionnaire results about SenStick

Question contents ScoreQ1 Do you think that SenStick will be useful for IoTresearch 49

Q2 Do you think that SenStick will be useful for IoTeducation 47

Q3 Do you want to use SenStick again 49Q4 Do you think that SenStick was easy to use 41Q5 Are you satisfied with specification of SenStick 41

hardware software and 3D case data The main purpose ofour platform is to simplify the sensorization of our personalbelongings

The hardware of SenStick is maybe one of the worldrsquossmallest sensor boards which includes 8 kinds of sensorsand BLE In addition to the hardware we also developedsupport software for managing monitoring recording andviewing the hardware By providing 3D case data all theresearchers can measure every tiny activity in daily life easilyand efficiently For an educational use it is also useful forunderstanding sensors and prototyping the desired systemincluding tiny censored things

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

This work is supported in part by JST PRESTO and JSPSKAKENHI (Grant no 17J10021) and ldquoResearch and Devel-opment of Innovative Network Technologies to Create theFuturerdquo the Commissioned Research of National Instituteof Information and Communications Technology (NICT)Japan

16 Journal of Sensors

References

[1] O Brdiczka M Langet J Maisonnasse and J L CrowleyldquoDetecting human behavior models from multimodal observa-tion in a smart homerdquo IEEE Transactions on Automation Scienceand Engineering vol 6 no 4 pp 588ndash597 2009

[2] J Chen A Kam J Zhang N Liu and L Shue ldquoBathroomactivity monitoring based on soundrdquo in Pervasive ComputingThird International Conference PERVASIVE 2005 MunichGermanyMay 8-13 2005 Proceedings vol 3468 ofLectureNotesin Computer Science pp 47ndash61 Springer Berlin Germany2002

[3] G Bauer K Stockinger and P Lukowicz ldquoRecognizing theUse-Mode of Kitchen Appliances from Their Current Consump-tionrdquo in Smart Sensing and Context vol 5741 of Lecture Notesin Computer Science pp 163ndash176 Springer Berlin HeidelbergBerlin Heidelberg 2009

[4] K Ouchi and M Doi ldquoIndoor-outdoor activity recognition bya smartphonerdquo in Proceedings of the the 2012 ACM Conferencep 537 Pittsburgh Pennsylvania September 2012

[5] A Anjum and M U Ilyas ldquoActivity recognition using smart-phone sensorsrdquo in Proceedings of the IEEE 10th ConsumerCommunications and Networking Conference (CCNC rsquo13) pp914ndash919 IEEE January 2013

[6] O D Lara and M A Labrador ldquoA survey on human activityrecognition using wearable sensorsrdquo IEEE CommunicationsSurveys amp Tutorials vol 15 no 3 pp 1192ndash1209 2013

[7] ldquoJins memerdquo httpsjins-memecom[8] Y Matsuda T Ishioka T Hasegawa et al ldquoWaistonBelt A belt

for monitoring your real abdominal circumference foreverrdquo inProceedings of the ACM International Joint Conference on Perva-sive and Ubiquitous Computing and the 2015 ACM InternationalSymposium on Wearable Computers UbiComp and ISWC 2015pp 329ndash332 September 2015

[9] M Kurahashi K Murao T Terada and M Tsukamoto ldquoPer-sonal identification system based on rotation of toilet paperrollsrdquo in Proceedings of the IEEE International Conference onPervasive Computing and CommunicationsWorkshops (PerComWorkshops rsquo17) pp 521ndash526 IEEE Kona Hawaii USA March2017

[10] ldquoHapiforkrdquo httpswwwhapicom[11] Y Arakawa ldquoSenStick Sensorize every thingsrdquo in Proceedings of

the ACM International Joint Conference on Pervasive and Ubiq-uitous Computing and the 2015 ACM International SymposiumonWearable Computers UbiComp and ISWC 2015 pp 349ndash352September 2015

[12] Y Nakamura T Kanehira Y Arakawa and K YasumotoldquoSenStick 2 Ultra tiny all-in-one sensor with wireless chargingrdquoinProceedings of the 2016ACM International Joint Conference onPervasive and Ubiquitous Computing UbiComp 2016 pp 337ndash340 September 2016

[13] ldquoSensortag cc2541 (texas instruments)rdquo httpwwwticomtoolcc2541dk-sensor

[14] ldquoIot smart modulerdquo httpwwwalpscomjiotsmart[15] ldquoTsnd121 (atr-promotions)rdquo httpwwwatr-pcomproducts

TSND121html[16] ldquoWax9 (axivity)rdquo httpaxivitycomproductwax9[17] Uk biobank ldquoUk biobankrdquo httpwwwukbiobankacuk[18] J C Seidell and J Halberstadt ldquoThe global burden of obesity

and the challenges of preventionrdquo Annals of Nutrition andMetabolism vol 66 supplement 2 pp 7ndash12 2015

[19] ldquoWeltThe smart belt for fashion and healthrdquo httpswwwwelt-corpcom

[20] ldquoBelty power The belt your really deserverdquo httpswwwbeltyco

[21] H Nam J-H Kim and J-I Kim ldquoSmart Belt A wearabledevice for managing abdominal obesityrdquo in Proceedings of theInternational Conference on Big Data and Smart ComputingBigComp 2016 pp 430ndash434 January 2016

[22] K Moriya M Fujimoto Y Arakawa and K Yasumoto ldquoIndoorlocalization based on distance-illuminance model and activecontrol of lighting devicesrdquo in Proceedings of the 2016 Interna-tional Conference on Indoor Positioning and Indoor NavigationIPIN 2016 pp 1ndash6 October 2016

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal of

Volume 201

Submit your manuscripts athttpswwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

Page 11: SenStick: Comprehensive Sensing Platform with an Ultra ...downloads.hindawi.com/journals/js/2017/6308302.pdf · ResearchArticle SenStick: Comprehensive Sensing Platform with an Ultra

Journal of Sensors 11

C1

C2 C3

C4

Typical signal of each food

C1 rice amp dish C2 miso soup C3 noodles C4 shrimp

400

200

0

minus200

minus400

Figure 9 Eating activity recognition by sensorized chopsticks

SenStick

Waiston Belt

Figure 10 Waiston Belt based on SenStick

Table 5 Experimental result (food type)

Food type Number ofpieces of data Precision Recall 119865-Measure

C1 rice and dish 3011 0894 0945 0919C2 miso soup 2187 0907 0841 0873C3 noodles 2761 088 0896 0888C4 shrimp 2091 0907 088 0893

0896 0896 0895

to develop a stylish prototype of Waiston Belt shown inFigure 10 quickly Since the battery life of SenStick is 15 hourswith a small battery (60mAh) we can sense continuously inour daily lives As shown in Figure 11 it is possible to estimatevarious usersrsquo contexts (How much did you walk Are youusing the stairs Is posture good at work) by analyzing timeseries of sensing data obtained from the sensorized belt

An important point in this use case is that usersrsquo contextscan be estimated by combining multiple sensing data For

Table 6 Confusion matrix of food type recognition

PredictedC1 C2 C3 C4

ActualC1 rice and dish 094 004 001 000C2 miso soup 012 084 004 000C3 noodles 002 002 090 006C4 shrimp 001 001 011 088

example climbing stairs is estimated from the changes ofacceleration and atmospheric pressure The change of thehumidity data will be helpful to detect that a user enteredan air-conditioned room A SenStick makes it possible toeasily carry out research on behavior recognition with acombination of such multiple sensors

We introduce basic experiments about the posture recog-nition during deskwork by using this beltThe purpose of thisexperiment is to identify the posture type from the attitude

12 Journal of Sensors

Enter the lab

HumidityTemp

Humidity dropping

Pressure dropping

Working

Walking WalkingClimbing stairs Sitting (good posture) Sitting (bad posture)

AccsXAccsYAccsZ

60

40

20

1003

1002

1001

1000

999

25

2

15

1

05

0

minus15

minus1

minus05

Air pressure

Figure 11 Time series of sensing data in daily activity

C1 good posture C2 bad posture 1

C3 bad posture 2 C4 bad posture 3

Figure 12 Time series of sensing data in posture monitoring

angle of the belt calculated from 3 axial acceleration sensorsWe set the sensing rate as 10Hz and collected 6000 samples(10min) for each posture We assumed 4 classes (C1 goodposture C2 bad posture 1 C3 bad posture 2 and C4 badposture 3) as shown in Figure 12

In this experiment we calculated the recognition accu-racy of posture type classification by using Random Forestclassifier of Weka Tables 7 and 8 show the classificationresults The average recognition accuracy was 997 Inthe future based on the result of posture estimation we

Journal of Sensors 13

Sensorized glasses

Figure 13 Sensorized glasses

Table 7 Experimental result (posture type)

Posture type Number ofpieces of data Precision Recall 119865-Measure

C1 good posture 6000 1000 1000 1000C2 bad posture 1 6000 0994 0994 0994C3 bad posture 2 6000 0994 0994 0994C4 bad posture 3 6000 0999 0999 0999

0997 0997 0997

will develop a software application that suggests correctinghisher bad posture to a right posture during desk work

43 Indoor Localization by Sensorized Glasses We have pro-posed an indoor localization system using illuminance-basedtrilateration method [22] In this system we assume that theperson wears glasses having an illuminance sensor as shownin Figure 13 and controllable light sources are deployed in thetarget environment Our system controls lights andmeasuresthe variation of illuminance at the glasses As shown inFigure 14 the distances (1198891ndash1198893) from each light (1198971ndash1198973) to theuser (119906) are estimated from the distance-illumination modelconstructed in advance and userrsquos position is estimated basedon trilateration method using estimated distances

As a result of an experiment where three controllablelight sources were deployed in a room we confirmed thatour system could estimate the position within 05m erroron average By using SenStick we could easily develop thesensorized glasses equipped with illuminance sensor

44 Sensorized Airsoft Gun We have developed the sen-sorized airsoft gun for activity recognition in airsoft gameas shown in Figure 15 Airsoft game is a sport in whichparticipants eliminate opponents by hitting each other usingreplica weapons called airsoft guns The graph of a typicalsignal from the sensorized airsoft gun is shown in Figure 16A user can count the number of shots of a ball by attachingSenStick to his airsoft gun

45 Another Example Studentrsquos Unique Achievements andSurvey Results SenStick is also used in a lecture of IoT

Table 8 Confusion matrix of posture type recognition

PredictedC1 C2 C3 C4

ActualC1 good posture 1000 0000 0000 0000C2 bad posture 1 0000 0994 0006 0001C3 bad posture 2 0000 0006 0994 0000C4 bad posture 3 0000 0001 0000 0999

prototyping Students realize their unique IoT devices easilyby using SenSticks Here we introduce some of the developedIoT devices and survey results about the usefulness ofSenStick answered by students

Figure 17 shows a sensorized pencil with two SenSticksThe sensorized pencil is developed to recognize the charac-ters written by this pencil Even though two SenSticks areembedded to detect precise movement of pencil the size ofthe pencil is reasonable

Figure 18 shows a sensorized name tag anddigital signageSince we changed the battery from 60mAh to 900mAh thetag keeps running more than one week It aims to recordemployeesrsquo or studentsrsquo daily activities to support their healthThe digital signage informs the user who wears a sensorizedname tag about the userrsquos activity data when the user standsin front of it

Figure 19 shows a sensorized spoon used in the digitalfish catching game for children In this game the sensorizedspoon detects the scooping movement of the hand Thuschildren can catch the digital fish displayed by the projectionmapping by using sensorized spoonThis systemwas actuallyused at the universityrsquos open campus event and around100 children enjoyed this game and showed interest in IoTtechnology

The survey results obtained from the ten graduate stu-dents (ninemales and one female) who developed prototypesas mentioned above are shown in Table 9 A questionnaireis a five-grade form (5 strongly agree 4 agree a little 3neither agree nor disagree 2 disagree a little and 1 stronglydisagree) From the results of questions 1 and 2 manyparticipants think that SenStick platform is useful for IoT

14 Journal of Sensors

Position

Distance illumination model

Using trilateration method

a

Light 3

Light 2

Light 1

L = minus421d + 1496

L = minus063d + 501

05 1 15 2 25 3 35 4 450Distance between the illuminance sensor and the lighting device (m)

02468

1012141618

Illum

inan

ce v

alue

(lux

)

Distanceu

d1

d2

d3

l1 p1 x1

l3 p3 x3

l2 p2 x2

Figure 14 Indoor localization by sensorized glasses

Sensorized airsoft gun

Figure 15 Sensorized airsoft gun

research and IoT education Also the results of question 3show that many students are interested in the SenStick andwant to use it again From the results of questions 4 and 5we can see that usability and specification of SenStick arerelatively high There were opinions about the usefulness ofSenStick platform such as ldquoI could quickly try out ideas of IoTprototypingrdquo ldquoIt was easy to collect sensing datardquo and ldquoSinceSenStick is small it was easily attached tomost of the thingsrdquoOn the other hand there were opinions about improvements

such as ldquoI want to use higher sensing frequency (50ndash100Hz)rdquoldquoI want to process a sensing data on SenStickrdquo and ldquoI want tomodify the firmware itself depending on the application ofthe IoT devicerdquo

5 Conclusion

In this paper we have introduced a smart and comprehensivesensing platform called SenStick which is composed of

Journal of Sensors 15

Typical signal of airso gun

Hold a gun Ready

Shoot

1 2 3 4 5 6 70

00

30

60

91

21

51

82

12

42

73

03

33

63

94

24

54

85

15

45

76

06

36

66

97

27

57

88

18

48

79

09

39

69

910

210

510

811

111

411

712

012

312

612

913

2

minus600

minus400

minus200

0

200

400

600

800

acc_xacc_yacc_z

gyro_xgyro_y

acc_synthesis gyro_zgyro_synthesis

Figure 16 Time series of sensing data in shooting an airsoft gun

Sensorized pencil

Figure 17 Sensorized pencil

Sensorized name tagVisualization of sensor data

Figure 18 Sensorized name tag

Sensorized spoon Digital fishing game

Figure 19 Sensorized spoon

Table 9 Questionnaire results about SenStick

Question contents ScoreQ1 Do you think that SenStick will be useful for IoTresearch 49

Q2 Do you think that SenStick will be useful for IoTeducation 47

Q3 Do you want to use SenStick again 49Q4 Do you think that SenStick was easy to use 41Q5 Are you satisfied with specification of SenStick 41

hardware software and 3D case data The main purpose ofour platform is to simplify the sensorization of our personalbelongings

The hardware of SenStick is maybe one of the worldrsquossmallest sensor boards which includes 8 kinds of sensorsand BLE In addition to the hardware we also developedsupport software for managing monitoring recording andviewing the hardware By providing 3D case data all theresearchers can measure every tiny activity in daily life easilyand efficiently For an educational use it is also useful forunderstanding sensors and prototyping the desired systemincluding tiny censored things

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

This work is supported in part by JST PRESTO and JSPSKAKENHI (Grant no 17J10021) and ldquoResearch and Devel-opment of Innovative Network Technologies to Create theFuturerdquo the Commissioned Research of National Instituteof Information and Communications Technology (NICT)Japan

16 Journal of Sensors

References

[1] O Brdiczka M Langet J Maisonnasse and J L CrowleyldquoDetecting human behavior models from multimodal observa-tion in a smart homerdquo IEEE Transactions on Automation Scienceand Engineering vol 6 no 4 pp 588ndash597 2009

[2] J Chen A Kam J Zhang N Liu and L Shue ldquoBathroomactivity monitoring based on soundrdquo in Pervasive ComputingThird International Conference PERVASIVE 2005 MunichGermanyMay 8-13 2005 Proceedings vol 3468 ofLectureNotesin Computer Science pp 47ndash61 Springer Berlin Germany2002

[3] G Bauer K Stockinger and P Lukowicz ldquoRecognizing theUse-Mode of Kitchen Appliances from Their Current Consump-tionrdquo in Smart Sensing and Context vol 5741 of Lecture Notesin Computer Science pp 163ndash176 Springer Berlin HeidelbergBerlin Heidelberg 2009

[4] K Ouchi and M Doi ldquoIndoor-outdoor activity recognition bya smartphonerdquo in Proceedings of the the 2012 ACM Conferencep 537 Pittsburgh Pennsylvania September 2012

[5] A Anjum and M U Ilyas ldquoActivity recognition using smart-phone sensorsrdquo in Proceedings of the IEEE 10th ConsumerCommunications and Networking Conference (CCNC rsquo13) pp914ndash919 IEEE January 2013

[6] O D Lara and M A Labrador ldquoA survey on human activityrecognition using wearable sensorsrdquo IEEE CommunicationsSurveys amp Tutorials vol 15 no 3 pp 1192ndash1209 2013

[7] ldquoJins memerdquo httpsjins-memecom[8] Y Matsuda T Ishioka T Hasegawa et al ldquoWaistonBelt A belt

for monitoring your real abdominal circumference foreverrdquo inProceedings of the ACM International Joint Conference on Perva-sive and Ubiquitous Computing and the 2015 ACM InternationalSymposium on Wearable Computers UbiComp and ISWC 2015pp 329ndash332 September 2015

[9] M Kurahashi K Murao T Terada and M Tsukamoto ldquoPer-sonal identification system based on rotation of toilet paperrollsrdquo in Proceedings of the IEEE International Conference onPervasive Computing and CommunicationsWorkshops (PerComWorkshops rsquo17) pp 521ndash526 IEEE Kona Hawaii USA March2017

[10] ldquoHapiforkrdquo httpswwwhapicom[11] Y Arakawa ldquoSenStick Sensorize every thingsrdquo in Proceedings of

the ACM International Joint Conference on Pervasive and Ubiq-uitous Computing and the 2015 ACM International SymposiumonWearable Computers UbiComp and ISWC 2015 pp 349ndash352September 2015

[12] Y Nakamura T Kanehira Y Arakawa and K YasumotoldquoSenStick 2 Ultra tiny all-in-one sensor with wireless chargingrdquoinProceedings of the 2016ACM International Joint Conference onPervasive and Ubiquitous Computing UbiComp 2016 pp 337ndash340 September 2016

[13] ldquoSensortag cc2541 (texas instruments)rdquo httpwwwticomtoolcc2541dk-sensor

[14] ldquoIot smart modulerdquo httpwwwalpscomjiotsmart[15] ldquoTsnd121 (atr-promotions)rdquo httpwwwatr-pcomproducts

TSND121html[16] ldquoWax9 (axivity)rdquo httpaxivitycomproductwax9[17] Uk biobank ldquoUk biobankrdquo httpwwwukbiobankacuk[18] J C Seidell and J Halberstadt ldquoThe global burden of obesity

and the challenges of preventionrdquo Annals of Nutrition andMetabolism vol 66 supplement 2 pp 7ndash12 2015

[19] ldquoWeltThe smart belt for fashion and healthrdquo httpswwwwelt-corpcom

[20] ldquoBelty power The belt your really deserverdquo httpswwwbeltyco

[21] H Nam J-H Kim and J-I Kim ldquoSmart Belt A wearabledevice for managing abdominal obesityrdquo in Proceedings of theInternational Conference on Big Data and Smart ComputingBigComp 2016 pp 430ndash434 January 2016

[22] K Moriya M Fujimoto Y Arakawa and K Yasumoto ldquoIndoorlocalization based on distance-illuminance model and activecontrol of lighting devicesrdquo in Proceedings of the 2016 Interna-tional Conference on Indoor Positioning and Indoor NavigationIPIN 2016 pp 1ndash6 October 2016

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal of

Volume 201

Submit your manuscripts athttpswwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

Page 12: SenStick: Comprehensive Sensing Platform with an Ultra ...downloads.hindawi.com/journals/js/2017/6308302.pdf · ResearchArticle SenStick: Comprehensive Sensing Platform with an Ultra

12 Journal of Sensors

Enter the lab

HumidityTemp

Humidity dropping

Pressure dropping

Working

Walking WalkingClimbing stairs Sitting (good posture) Sitting (bad posture)

AccsXAccsYAccsZ

60

40

20

1003

1002

1001

1000

999

25

2

15

1

05

0

minus15

minus1

minus05

Air pressure

Figure 11 Time series of sensing data in daily activity

C1 good posture C2 bad posture 1

C3 bad posture 2 C4 bad posture 3

Figure 12 Time series of sensing data in posture monitoring

angle of the belt calculated from 3 axial acceleration sensorsWe set the sensing rate as 10Hz and collected 6000 samples(10min) for each posture We assumed 4 classes (C1 goodposture C2 bad posture 1 C3 bad posture 2 and C4 badposture 3) as shown in Figure 12

In this experiment we calculated the recognition accu-racy of posture type classification by using Random Forestclassifier of Weka Tables 7 and 8 show the classificationresults The average recognition accuracy was 997 Inthe future based on the result of posture estimation we

Journal of Sensors 13

Sensorized glasses

Figure 13 Sensorized glasses

Table 7 Experimental result (posture type)

Posture type Number ofpieces of data Precision Recall 119865-Measure

C1 good posture 6000 1000 1000 1000C2 bad posture 1 6000 0994 0994 0994C3 bad posture 2 6000 0994 0994 0994C4 bad posture 3 6000 0999 0999 0999

0997 0997 0997

will develop a software application that suggests correctinghisher bad posture to a right posture during desk work

43 Indoor Localization by Sensorized Glasses We have pro-posed an indoor localization system using illuminance-basedtrilateration method [22] In this system we assume that theperson wears glasses having an illuminance sensor as shownin Figure 13 and controllable light sources are deployed in thetarget environment Our system controls lights andmeasuresthe variation of illuminance at the glasses As shown inFigure 14 the distances (1198891ndash1198893) from each light (1198971ndash1198973) to theuser (119906) are estimated from the distance-illumination modelconstructed in advance and userrsquos position is estimated basedon trilateration method using estimated distances

As a result of an experiment where three controllablelight sources were deployed in a room we confirmed thatour system could estimate the position within 05m erroron average By using SenStick we could easily develop thesensorized glasses equipped with illuminance sensor

44 Sensorized Airsoft Gun We have developed the sen-sorized airsoft gun for activity recognition in airsoft gameas shown in Figure 15 Airsoft game is a sport in whichparticipants eliminate opponents by hitting each other usingreplica weapons called airsoft guns The graph of a typicalsignal from the sensorized airsoft gun is shown in Figure 16A user can count the number of shots of a ball by attachingSenStick to his airsoft gun

45 Another Example Studentrsquos Unique Achievements andSurvey Results SenStick is also used in a lecture of IoT

Table 8 Confusion matrix of posture type recognition

PredictedC1 C2 C3 C4

ActualC1 good posture 1000 0000 0000 0000C2 bad posture 1 0000 0994 0006 0001C3 bad posture 2 0000 0006 0994 0000C4 bad posture 3 0000 0001 0000 0999

prototyping Students realize their unique IoT devices easilyby using SenSticks Here we introduce some of the developedIoT devices and survey results about the usefulness ofSenStick answered by students

Figure 17 shows a sensorized pencil with two SenSticksThe sensorized pencil is developed to recognize the charac-ters written by this pencil Even though two SenSticks areembedded to detect precise movement of pencil the size ofthe pencil is reasonable

Figure 18 shows a sensorized name tag anddigital signageSince we changed the battery from 60mAh to 900mAh thetag keeps running more than one week It aims to recordemployeesrsquo or studentsrsquo daily activities to support their healthThe digital signage informs the user who wears a sensorizedname tag about the userrsquos activity data when the user standsin front of it

Figure 19 shows a sensorized spoon used in the digitalfish catching game for children In this game the sensorizedspoon detects the scooping movement of the hand Thuschildren can catch the digital fish displayed by the projectionmapping by using sensorized spoonThis systemwas actuallyused at the universityrsquos open campus event and around100 children enjoyed this game and showed interest in IoTtechnology

The survey results obtained from the ten graduate stu-dents (ninemales and one female) who developed prototypesas mentioned above are shown in Table 9 A questionnaireis a five-grade form (5 strongly agree 4 agree a little 3neither agree nor disagree 2 disagree a little and 1 stronglydisagree) From the results of questions 1 and 2 manyparticipants think that SenStick platform is useful for IoT

14 Journal of Sensors

Position

Distance illumination model

Using trilateration method

a

Light 3

Light 2

Light 1

L = minus421d + 1496

L = minus063d + 501

05 1 15 2 25 3 35 4 450Distance between the illuminance sensor and the lighting device (m)

02468

1012141618

Illum

inan

ce v

alue

(lux

)

Distanceu

d1

d2

d3

l1 p1 x1

l3 p3 x3

l2 p2 x2

Figure 14 Indoor localization by sensorized glasses

Sensorized airsoft gun

Figure 15 Sensorized airsoft gun

research and IoT education Also the results of question 3show that many students are interested in the SenStick andwant to use it again From the results of questions 4 and 5we can see that usability and specification of SenStick arerelatively high There were opinions about the usefulness ofSenStick platform such as ldquoI could quickly try out ideas of IoTprototypingrdquo ldquoIt was easy to collect sensing datardquo and ldquoSinceSenStick is small it was easily attached tomost of the thingsrdquoOn the other hand there were opinions about improvements

such as ldquoI want to use higher sensing frequency (50ndash100Hz)rdquoldquoI want to process a sensing data on SenStickrdquo and ldquoI want tomodify the firmware itself depending on the application ofthe IoT devicerdquo

5 Conclusion

In this paper we have introduced a smart and comprehensivesensing platform called SenStick which is composed of

Journal of Sensors 15

Typical signal of airso gun

Hold a gun Ready

Shoot

1 2 3 4 5 6 70

00

30

60

91

21

51

82

12

42

73

03

33

63

94

24

54

85

15

45

76

06

36

66

97

27

57

88

18

48

79

09

39

69

910

210

510

811

111

411

712

012

312

612

913

2

minus600

minus400

minus200

0

200

400

600

800

acc_xacc_yacc_z

gyro_xgyro_y

acc_synthesis gyro_zgyro_synthesis

Figure 16 Time series of sensing data in shooting an airsoft gun

Sensorized pencil

Figure 17 Sensorized pencil

Sensorized name tagVisualization of sensor data

Figure 18 Sensorized name tag

Sensorized spoon Digital fishing game

Figure 19 Sensorized spoon

Table 9 Questionnaire results about SenStick

Question contents ScoreQ1 Do you think that SenStick will be useful for IoTresearch 49

Q2 Do you think that SenStick will be useful for IoTeducation 47

Q3 Do you want to use SenStick again 49Q4 Do you think that SenStick was easy to use 41Q5 Are you satisfied with specification of SenStick 41

hardware software and 3D case data The main purpose ofour platform is to simplify the sensorization of our personalbelongings

The hardware of SenStick is maybe one of the worldrsquossmallest sensor boards which includes 8 kinds of sensorsand BLE In addition to the hardware we also developedsupport software for managing monitoring recording andviewing the hardware By providing 3D case data all theresearchers can measure every tiny activity in daily life easilyand efficiently For an educational use it is also useful forunderstanding sensors and prototyping the desired systemincluding tiny censored things

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

This work is supported in part by JST PRESTO and JSPSKAKENHI (Grant no 17J10021) and ldquoResearch and Devel-opment of Innovative Network Technologies to Create theFuturerdquo the Commissioned Research of National Instituteof Information and Communications Technology (NICT)Japan

16 Journal of Sensors

References

[1] O Brdiczka M Langet J Maisonnasse and J L CrowleyldquoDetecting human behavior models from multimodal observa-tion in a smart homerdquo IEEE Transactions on Automation Scienceand Engineering vol 6 no 4 pp 588ndash597 2009

[2] J Chen A Kam J Zhang N Liu and L Shue ldquoBathroomactivity monitoring based on soundrdquo in Pervasive ComputingThird International Conference PERVASIVE 2005 MunichGermanyMay 8-13 2005 Proceedings vol 3468 ofLectureNotesin Computer Science pp 47ndash61 Springer Berlin Germany2002

[3] G Bauer K Stockinger and P Lukowicz ldquoRecognizing theUse-Mode of Kitchen Appliances from Their Current Consump-tionrdquo in Smart Sensing and Context vol 5741 of Lecture Notesin Computer Science pp 163ndash176 Springer Berlin HeidelbergBerlin Heidelberg 2009

[4] K Ouchi and M Doi ldquoIndoor-outdoor activity recognition bya smartphonerdquo in Proceedings of the the 2012 ACM Conferencep 537 Pittsburgh Pennsylvania September 2012

[5] A Anjum and M U Ilyas ldquoActivity recognition using smart-phone sensorsrdquo in Proceedings of the IEEE 10th ConsumerCommunications and Networking Conference (CCNC rsquo13) pp914ndash919 IEEE January 2013

[6] O D Lara and M A Labrador ldquoA survey on human activityrecognition using wearable sensorsrdquo IEEE CommunicationsSurveys amp Tutorials vol 15 no 3 pp 1192ndash1209 2013

[7] ldquoJins memerdquo httpsjins-memecom[8] Y Matsuda T Ishioka T Hasegawa et al ldquoWaistonBelt A belt

for monitoring your real abdominal circumference foreverrdquo inProceedings of the ACM International Joint Conference on Perva-sive and Ubiquitous Computing and the 2015 ACM InternationalSymposium on Wearable Computers UbiComp and ISWC 2015pp 329ndash332 September 2015

[9] M Kurahashi K Murao T Terada and M Tsukamoto ldquoPer-sonal identification system based on rotation of toilet paperrollsrdquo in Proceedings of the IEEE International Conference onPervasive Computing and CommunicationsWorkshops (PerComWorkshops rsquo17) pp 521ndash526 IEEE Kona Hawaii USA March2017

[10] ldquoHapiforkrdquo httpswwwhapicom[11] Y Arakawa ldquoSenStick Sensorize every thingsrdquo in Proceedings of

the ACM International Joint Conference on Pervasive and Ubiq-uitous Computing and the 2015 ACM International SymposiumonWearable Computers UbiComp and ISWC 2015 pp 349ndash352September 2015

[12] Y Nakamura T Kanehira Y Arakawa and K YasumotoldquoSenStick 2 Ultra tiny all-in-one sensor with wireless chargingrdquoinProceedings of the 2016ACM International Joint Conference onPervasive and Ubiquitous Computing UbiComp 2016 pp 337ndash340 September 2016

[13] ldquoSensortag cc2541 (texas instruments)rdquo httpwwwticomtoolcc2541dk-sensor

[14] ldquoIot smart modulerdquo httpwwwalpscomjiotsmart[15] ldquoTsnd121 (atr-promotions)rdquo httpwwwatr-pcomproducts

TSND121html[16] ldquoWax9 (axivity)rdquo httpaxivitycomproductwax9[17] Uk biobank ldquoUk biobankrdquo httpwwwukbiobankacuk[18] J C Seidell and J Halberstadt ldquoThe global burden of obesity

and the challenges of preventionrdquo Annals of Nutrition andMetabolism vol 66 supplement 2 pp 7ndash12 2015

[19] ldquoWeltThe smart belt for fashion and healthrdquo httpswwwwelt-corpcom

[20] ldquoBelty power The belt your really deserverdquo httpswwwbeltyco

[21] H Nam J-H Kim and J-I Kim ldquoSmart Belt A wearabledevice for managing abdominal obesityrdquo in Proceedings of theInternational Conference on Big Data and Smart ComputingBigComp 2016 pp 430ndash434 January 2016

[22] K Moriya M Fujimoto Y Arakawa and K Yasumoto ldquoIndoorlocalization based on distance-illuminance model and activecontrol of lighting devicesrdquo in Proceedings of the 2016 Interna-tional Conference on Indoor Positioning and Indoor NavigationIPIN 2016 pp 1ndash6 October 2016

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal of

Volume 201

Submit your manuscripts athttpswwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

Page 13: SenStick: Comprehensive Sensing Platform with an Ultra ...downloads.hindawi.com/journals/js/2017/6308302.pdf · ResearchArticle SenStick: Comprehensive Sensing Platform with an Ultra

Journal of Sensors 13

Sensorized glasses

Figure 13 Sensorized glasses

Table 7 Experimental result (posture type)

Posture type Number ofpieces of data Precision Recall 119865-Measure

C1 good posture 6000 1000 1000 1000C2 bad posture 1 6000 0994 0994 0994C3 bad posture 2 6000 0994 0994 0994C4 bad posture 3 6000 0999 0999 0999

0997 0997 0997

will develop a software application that suggests correctinghisher bad posture to a right posture during desk work

43 Indoor Localization by Sensorized Glasses We have pro-posed an indoor localization system using illuminance-basedtrilateration method [22] In this system we assume that theperson wears glasses having an illuminance sensor as shownin Figure 13 and controllable light sources are deployed in thetarget environment Our system controls lights andmeasuresthe variation of illuminance at the glasses As shown inFigure 14 the distances (1198891ndash1198893) from each light (1198971ndash1198973) to theuser (119906) are estimated from the distance-illumination modelconstructed in advance and userrsquos position is estimated basedon trilateration method using estimated distances

As a result of an experiment where three controllablelight sources were deployed in a room we confirmed thatour system could estimate the position within 05m erroron average By using SenStick we could easily develop thesensorized glasses equipped with illuminance sensor

44 Sensorized Airsoft Gun We have developed the sen-sorized airsoft gun for activity recognition in airsoft gameas shown in Figure 15 Airsoft game is a sport in whichparticipants eliminate opponents by hitting each other usingreplica weapons called airsoft guns The graph of a typicalsignal from the sensorized airsoft gun is shown in Figure 16A user can count the number of shots of a ball by attachingSenStick to his airsoft gun

45 Another Example Studentrsquos Unique Achievements andSurvey Results SenStick is also used in a lecture of IoT

Table 8 Confusion matrix of posture type recognition

PredictedC1 C2 C3 C4

ActualC1 good posture 1000 0000 0000 0000C2 bad posture 1 0000 0994 0006 0001C3 bad posture 2 0000 0006 0994 0000C4 bad posture 3 0000 0001 0000 0999

prototyping Students realize their unique IoT devices easilyby using SenSticks Here we introduce some of the developedIoT devices and survey results about the usefulness ofSenStick answered by students

Figure 17 shows a sensorized pencil with two SenSticksThe sensorized pencil is developed to recognize the charac-ters written by this pencil Even though two SenSticks areembedded to detect precise movement of pencil the size ofthe pencil is reasonable

Figure 18 shows a sensorized name tag anddigital signageSince we changed the battery from 60mAh to 900mAh thetag keeps running more than one week It aims to recordemployeesrsquo or studentsrsquo daily activities to support their healthThe digital signage informs the user who wears a sensorizedname tag about the userrsquos activity data when the user standsin front of it

Figure 19 shows a sensorized spoon used in the digitalfish catching game for children In this game the sensorizedspoon detects the scooping movement of the hand Thuschildren can catch the digital fish displayed by the projectionmapping by using sensorized spoonThis systemwas actuallyused at the universityrsquos open campus event and around100 children enjoyed this game and showed interest in IoTtechnology

The survey results obtained from the ten graduate stu-dents (ninemales and one female) who developed prototypesas mentioned above are shown in Table 9 A questionnaireis a five-grade form (5 strongly agree 4 agree a little 3neither agree nor disagree 2 disagree a little and 1 stronglydisagree) From the results of questions 1 and 2 manyparticipants think that SenStick platform is useful for IoT

14 Journal of Sensors

Position

Distance illumination model

Using trilateration method

a

Light 3

Light 2

Light 1

L = minus421d + 1496

L = minus063d + 501

05 1 15 2 25 3 35 4 450Distance between the illuminance sensor and the lighting device (m)

02468

1012141618

Illum

inan

ce v

alue

(lux

)

Distanceu

d1

d2

d3

l1 p1 x1

l3 p3 x3

l2 p2 x2

Figure 14 Indoor localization by sensorized glasses

Sensorized airsoft gun

Figure 15 Sensorized airsoft gun

research and IoT education Also the results of question 3show that many students are interested in the SenStick andwant to use it again From the results of questions 4 and 5we can see that usability and specification of SenStick arerelatively high There were opinions about the usefulness ofSenStick platform such as ldquoI could quickly try out ideas of IoTprototypingrdquo ldquoIt was easy to collect sensing datardquo and ldquoSinceSenStick is small it was easily attached tomost of the thingsrdquoOn the other hand there were opinions about improvements

such as ldquoI want to use higher sensing frequency (50ndash100Hz)rdquoldquoI want to process a sensing data on SenStickrdquo and ldquoI want tomodify the firmware itself depending on the application ofthe IoT devicerdquo

5 Conclusion

In this paper we have introduced a smart and comprehensivesensing platform called SenStick which is composed of

Journal of Sensors 15

Typical signal of airso gun

Hold a gun Ready

Shoot

1 2 3 4 5 6 70

00

30

60

91

21

51

82

12

42

73

03

33

63

94

24

54

85

15

45

76

06

36

66

97

27

57

88

18

48

79

09

39

69

910

210

510

811

111

411

712

012

312

612

913

2

minus600

minus400

minus200

0

200

400

600

800

acc_xacc_yacc_z

gyro_xgyro_y

acc_synthesis gyro_zgyro_synthesis

Figure 16 Time series of sensing data in shooting an airsoft gun

Sensorized pencil

Figure 17 Sensorized pencil

Sensorized name tagVisualization of sensor data

Figure 18 Sensorized name tag

Sensorized spoon Digital fishing game

Figure 19 Sensorized spoon

Table 9 Questionnaire results about SenStick

Question contents ScoreQ1 Do you think that SenStick will be useful for IoTresearch 49

Q2 Do you think that SenStick will be useful for IoTeducation 47

Q3 Do you want to use SenStick again 49Q4 Do you think that SenStick was easy to use 41Q5 Are you satisfied with specification of SenStick 41

hardware software and 3D case data The main purpose ofour platform is to simplify the sensorization of our personalbelongings

The hardware of SenStick is maybe one of the worldrsquossmallest sensor boards which includes 8 kinds of sensorsand BLE In addition to the hardware we also developedsupport software for managing monitoring recording andviewing the hardware By providing 3D case data all theresearchers can measure every tiny activity in daily life easilyand efficiently For an educational use it is also useful forunderstanding sensors and prototyping the desired systemincluding tiny censored things

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

This work is supported in part by JST PRESTO and JSPSKAKENHI (Grant no 17J10021) and ldquoResearch and Devel-opment of Innovative Network Technologies to Create theFuturerdquo the Commissioned Research of National Instituteof Information and Communications Technology (NICT)Japan

16 Journal of Sensors

References

[1] O Brdiczka M Langet J Maisonnasse and J L CrowleyldquoDetecting human behavior models from multimodal observa-tion in a smart homerdquo IEEE Transactions on Automation Scienceand Engineering vol 6 no 4 pp 588ndash597 2009

[2] J Chen A Kam J Zhang N Liu and L Shue ldquoBathroomactivity monitoring based on soundrdquo in Pervasive ComputingThird International Conference PERVASIVE 2005 MunichGermanyMay 8-13 2005 Proceedings vol 3468 ofLectureNotesin Computer Science pp 47ndash61 Springer Berlin Germany2002

[3] G Bauer K Stockinger and P Lukowicz ldquoRecognizing theUse-Mode of Kitchen Appliances from Their Current Consump-tionrdquo in Smart Sensing and Context vol 5741 of Lecture Notesin Computer Science pp 163ndash176 Springer Berlin HeidelbergBerlin Heidelberg 2009

[4] K Ouchi and M Doi ldquoIndoor-outdoor activity recognition bya smartphonerdquo in Proceedings of the the 2012 ACM Conferencep 537 Pittsburgh Pennsylvania September 2012

[5] A Anjum and M U Ilyas ldquoActivity recognition using smart-phone sensorsrdquo in Proceedings of the IEEE 10th ConsumerCommunications and Networking Conference (CCNC rsquo13) pp914ndash919 IEEE January 2013

[6] O D Lara and M A Labrador ldquoA survey on human activityrecognition using wearable sensorsrdquo IEEE CommunicationsSurveys amp Tutorials vol 15 no 3 pp 1192ndash1209 2013

[7] ldquoJins memerdquo httpsjins-memecom[8] Y Matsuda T Ishioka T Hasegawa et al ldquoWaistonBelt A belt

for monitoring your real abdominal circumference foreverrdquo inProceedings of the ACM International Joint Conference on Perva-sive and Ubiquitous Computing and the 2015 ACM InternationalSymposium on Wearable Computers UbiComp and ISWC 2015pp 329ndash332 September 2015

[9] M Kurahashi K Murao T Terada and M Tsukamoto ldquoPer-sonal identification system based on rotation of toilet paperrollsrdquo in Proceedings of the IEEE International Conference onPervasive Computing and CommunicationsWorkshops (PerComWorkshops rsquo17) pp 521ndash526 IEEE Kona Hawaii USA March2017

[10] ldquoHapiforkrdquo httpswwwhapicom[11] Y Arakawa ldquoSenStick Sensorize every thingsrdquo in Proceedings of

the ACM International Joint Conference on Pervasive and Ubiq-uitous Computing and the 2015 ACM International SymposiumonWearable Computers UbiComp and ISWC 2015 pp 349ndash352September 2015

[12] Y Nakamura T Kanehira Y Arakawa and K YasumotoldquoSenStick 2 Ultra tiny all-in-one sensor with wireless chargingrdquoinProceedings of the 2016ACM International Joint Conference onPervasive and Ubiquitous Computing UbiComp 2016 pp 337ndash340 September 2016

[13] ldquoSensortag cc2541 (texas instruments)rdquo httpwwwticomtoolcc2541dk-sensor

[14] ldquoIot smart modulerdquo httpwwwalpscomjiotsmart[15] ldquoTsnd121 (atr-promotions)rdquo httpwwwatr-pcomproducts

TSND121html[16] ldquoWax9 (axivity)rdquo httpaxivitycomproductwax9[17] Uk biobank ldquoUk biobankrdquo httpwwwukbiobankacuk[18] J C Seidell and J Halberstadt ldquoThe global burden of obesity

and the challenges of preventionrdquo Annals of Nutrition andMetabolism vol 66 supplement 2 pp 7ndash12 2015

[19] ldquoWeltThe smart belt for fashion and healthrdquo httpswwwwelt-corpcom

[20] ldquoBelty power The belt your really deserverdquo httpswwwbeltyco

[21] H Nam J-H Kim and J-I Kim ldquoSmart Belt A wearabledevice for managing abdominal obesityrdquo in Proceedings of theInternational Conference on Big Data and Smart ComputingBigComp 2016 pp 430ndash434 January 2016

[22] K Moriya M Fujimoto Y Arakawa and K Yasumoto ldquoIndoorlocalization based on distance-illuminance model and activecontrol of lighting devicesrdquo in Proceedings of the 2016 Interna-tional Conference on Indoor Positioning and Indoor NavigationIPIN 2016 pp 1ndash6 October 2016

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal of

Volume 201

Submit your manuscripts athttpswwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

Page 14: SenStick: Comprehensive Sensing Platform with an Ultra ...downloads.hindawi.com/journals/js/2017/6308302.pdf · ResearchArticle SenStick: Comprehensive Sensing Platform with an Ultra

14 Journal of Sensors

Position

Distance illumination model

Using trilateration method

a

Light 3

Light 2

Light 1

L = minus421d + 1496

L = minus063d + 501

05 1 15 2 25 3 35 4 450Distance between the illuminance sensor and the lighting device (m)

02468

1012141618

Illum

inan

ce v

alue

(lux

)

Distanceu

d1

d2

d3

l1 p1 x1

l3 p3 x3

l2 p2 x2

Figure 14 Indoor localization by sensorized glasses

Sensorized airsoft gun

Figure 15 Sensorized airsoft gun

research and IoT education Also the results of question 3show that many students are interested in the SenStick andwant to use it again From the results of questions 4 and 5we can see that usability and specification of SenStick arerelatively high There were opinions about the usefulness ofSenStick platform such as ldquoI could quickly try out ideas of IoTprototypingrdquo ldquoIt was easy to collect sensing datardquo and ldquoSinceSenStick is small it was easily attached tomost of the thingsrdquoOn the other hand there were opinions about improvements

such as ldquoI want to use higher sensing frequency (50ndash100Hz)rdquoldquoI want to process a sensing data on SenStickrdquo and ldquoI want tomodify the firmware itself depending on the application ofthe IoT devicerdquo

5 Conclusion

In this paper we have introduced a smart and comprehensivesensing platform called SenStick which is composed of

Journal of Sensors 15

Typical signal of airso gun

Hold a gun Ready

Shoot

1 2 3 4 5 6 70

00

30

60

91

21

51

82

12

42

73

03

33

63

94

24

54

85

15

45

76

06

36

66

97

27

57

88

18

48

79

09

39

69

910

210

510

811

111

411

712

012

312

612

913

2

minus600

minus400

minus200

0

200

400

600

800

acc_xacc_yacc_z

gyro_xgyro_y

acc_synthesis gyro_zgyro_synthesis

Figure 16 Time series of sensing data in shooting an airsoft gun

Sensorized pencil

Figure 17 Sensorized pencil

Sensorized name tagVisualization of sensor data

Figure 18 Sensorized name tag

Sensorized spoon Digital fishing game

Figure 19 Sensorized spoon

Table 9 Questionnaire results about SenStick

Question contents ScoreQ1 Do you think that SenStick will be useful for IoTresearch 49

Q2 Do you think that SenStick will be useful for IoTeducation 47

Q3 Do you want to use SenStick again 49Q4 Do you think that SenStick was easy to use 41Q5 Are you satisfied with specification of SenStick 41

hardware software and 3D case data The main purpose ofour platform is to simplify the sensorization of our personalbelongings

The hardware of SenStick is maybe one of the worldrsquossmallest sensor boards which includes 8 kinds of sensorsand BLE In addition to the hardware we also developedsupport software for managing monitoring recording andviewing the hardware By providing 3D case data all theresearchers can measure every tiny activity in daily life easilyand efficiently For an educational use it is also useful forunderstanding sensors and prototyping the desired systemincluding tiny censored things

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

This work is supported in part by JST PRESTO and JSPSKAKENHI (Grant no 17J10021) and ldquoResearch and Devel-opment of Innovative Network Technologies to Create theFuturerdquo the Commissioned Research of National Instituteof Information and Communications Technology (NICT)Japan

16 Journal of Sensors

References

[1] O Brdiczka M Langet J Maisonnasse and J L CrowleyldquoDetecting human behavior models from multimodal observa-tion in a smart homerdquo IEEE Transactions on Automation Scienceand Engineering vol 6 no 4 pp 588ndash597 2009

[2] J Chen A Kam J Zhang N Liu and L Shue ldquoBathroomactivity monitoring based on soundrdquo in Pervasive ComputingThird International Conference PERVASIVE 2005 MunichGermanyMay 8-13 2005 Proceedings vol 3468 ofLectureNotesin Computer Science pp 47ndash61 Springer Berlin Germany2002

[3] G Bauer K Stockinger and P Lukowicz ldquoRecognizing theUse-Mode of Kitchen Appliances from Their Current Consump-tionrdquo in Smart Sensing and Context vol 5741 of Lecture Notesin Computer Science pp 163ndash176 Springer Berlin HeidelbergBerlin Heidelberg 2009

[4] K Ouchi and M Doi ldquoIndoor-outdoor activity recognition bya smartphonerdquo in Proceedings of the the 2012 ACM Conferencep 537 Pittsburgh Pennsylvania September 2012

[5] A Anjum and M U Ilyas ldquoActivity recognition using smart-phone sensorsrdquo in Proceedings of the IEEE 10th ConsumerCommunications and Networking Conference (CCNC rsquo13) pp914ndash919 IEEE January 2013

[6] O D Lara and M A Labrador ldquoA survey on human activityrecognition using wearable sensorsrdquo IEEE CommunicationsSurveys amp Tutorials vol 15 no 3 pp 1192ndash1209 2013

[7] ldquoJins memerdquo httpsjins-memecom[8] Y Matsuda T Ishioka T Hasegawa et al ldquoWaistonBelt A belt

for monitoring your real abdominal circumference foreverrdquo inProceedings of the ACM International Joint Conference on Perva-sive and Ubiquitous Computing and the 2015 ACM InternationalSymposium on Wearable Computers UbiComp and ISWC 2015pp 329ndash332 September 2015

[9] M Kurahashi K Murao T Terada and M Tsukamoto ldquoPer-sonal identification system based on rotation of toilet paperrollsrdquo in Proceedings of the IEEE International Conference onPervasive Computing and CommunicationsWorkshops (PerComWorkshops rsquo17) pp 521ndash526 IEEE Kona Hawaii USA March2017

[10] ldquoHapiforkrdquo httpswwwhapicom[11] Y Arakawa ldquoSenStick Sensorize every thingsrdquo in Proceedings of

the ACM International Joint Conference on Pervasive and Ubiq-uitous Computing and the 2015 ACM International SymposiumonWearable Computers UbiComp and ISWC 2015 pp 349ndash352September 2015

[12] Y Nakamura T Kanehira Y Arakawa and K YasumotoldquoSenStick 2 Ultra tiny all-in-one sensor with wireless chargingrdquoinProceedings of the 2016ACM International Joint Conference onPervasive and Ubiquitous Computing UbiComp 2016 pp 337ndash340 September 2016

[13] ldquoSensortag cc2541 (texas instruments)rdquo httpwwwticomtoolcc2541dk-sensor

[14] ldquoIot smart modulerdquo httpwwwalpscomjiotsmart[15] ldquoTsnd121 (atr-promotions)rdquo httpwwwatr-pcomproducts

TSND121html[16] ldquoWax9 (axivity)rdquo httpaxivitycomproductwax9[17] Uk biobank ldquoUk biobankrdquo httpwwwukbiobankacuk[18] J C Seidell and J Halberstadt ldquoThe global burden of obesity

and the challenges of preventionrdquo Annals of Nutrition andMetabolism vol 66 supplement 2 pp 7ndash12 2015

[19] ldquoWeltThe smart belt for fashion and healthrdquo httpswwwwelt-corpcom

[20] ldquoBelty power The belt your really deserverdquo httpswwwbeltyco

[21] H Nam J-H Kim and J-I Kim ldquoSmart Belt A wearabledevice for managing abdominal obesityrdquo in Proceedings of theInternational Conference on Big Data and Smart ComputingBigComp 2016 pp 430ndash434 January 2016

[22] K Moriya M Fujimoto Y Arakawa and K Yasumoto ldquoIndoorlocalization based on distance-illuminance model and activecontrol of lighting devicesrdquo in Proceedings of the 2016 Interna-tional Conference on Indoor Positioning and Indoor NavigationIPIN 2016 pp 1ndash6 October 2016

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal of

Volume 201

Submit your manuscripts athttpswwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

Page 15: SenStick: Comprehensive Sensing Platform with an Ultra ...downloads.hindawi.com/journals/js/2017/6308302.pdf · ResearchArticle SenStick: Comprehensive Sensing Platform with an Ultra

Journal of Sensors 15

Typical signal of airso gun

Hold a gun Ready

Shoot

1 2 3 4 5 6 70

00

30

60

91

21

51

82

12

42

73

03

33

63

94

24

54

85

15

45

76

06

36

66

97

27

57

88

18

48

79

09

39

69

910

210

510

811

111

411

712

012

312

612

913

2

minus600

minus400

minus200

0

200

400

600

800

acc_xacc_yacc_z

gyro_xgyro_y

acc_synthesis gyro_zgyro_synthesis

Figure 16 Time series of sensing data in shooting an airsoft gun

Sensorized pencil

Figure 17 Sensorized pencil

Sensorized name tagVisualization of sensor data

Figure 18 Sensorized name tag

Sensorized spoon Digital fishing game

Figure 19 Sensorized spoon

Table 9 Questionnaire results about SenStick

Question contents ScoreQ1 Do you think that SenStick will be useful for IoTresearch 49

Q2 Do you think that SenStick will be useful for IoTeducation 47

Q3 Do you want to use SenStick again 49Q4 Do you think that SenStick was easy to use 41Q5 Are you satisfied with specification of SenStick 41

hardware software and 3D case data The main purpose ofour platform is to simplify the sensorization of our personalbelongings

The hardware of SenStick is maybe one of the worldrsquossmallest sensor boards which includes 8 kinds of sensorsand BLE In addition to the hardware we also developedsupport software for managing monitoring recording andviewing the hardware By providing 3D case data all theresearchers can measure every tiny activity in daily life easilyand efficiently For an educational use it is also useful forunderstanding sensors and prototyping the desired systemincluding tiny censored things

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

This work is supported in part by JST PRESTO and JSPSKAKENHI (Grant no 17J10021) and ldquoResearch and Devel-opment of Innovative Network Technologies to Create theFuturerdquo the Commissioned Research of National Instituteof Information and Communications Technology (NICT)Japan

16 Journal of Sensors

References

[1] O Brdiczka M Langet J Maisonnasse and J L CrowleyldquoDetecting human behavior models from multimodal observa-tion in a smart homerdquo IEEE Transactions on Automation Scienceand Engineering vol 6 no 4 pp 588ndash597 2009

[2] J Chen A Kam J Zhang N Liu and L Shue ldquoBathroomactivity monitoring based on soundrdquo in Pervasive ComputingThird International Conference PERVASIVE 2005 MunichGermanyMay 8-13 2005 Proceedings vol 3468 ofLectureNotesin Computer Science pp 47ndash61 Springer Berlin Germany2002

[3] G Bauer K Stockinger and P Lukowicz ldquoRecognizing theUse-Mode of Kitchen Appliances from Their Current Consump-tionrdquo in Smart Sensing and Context vol 5741 of Lecture Notesin Computer Science pp 163ndash176 Springer Berlin HeidelbergBerlin Heidelberg 2009

[4] K Ouchi and M Doi ldquoIndoor-outdoor activity recognition bya smartphonerdquo in Proceedings of the the 2012 ACM Conferencep 537 Pittsburgh Pennsylvania September 2012

[5] A Anjum and M U Ilyas ldquoActivity recognition using smart-phone sensorsrdquo in Proceedings of the IEEE 10th ConsumerCommunications and Networking Conference (CCNC rsquo13) pp914ndash919 IEEE January 2013

[6] O D Lara and M A Labrador ldquoA survey on human activityrecognition using wearable sensorsrdquo IEEE CommunicationsSurveys amp Tutorials vol 15 no 3 pp 1192ndash1209 2013

[7] ldquoJins memerdquo httpsjins-memecom[8] Y Matsuda T Ishioka T Hasegawa et al ldquoWaistonBelt A belt

for monitoring your real abdominal circumference foreverrdquo inProceedings of the ACM International Joint Conference on Perva-sive and Ubiquitous Computing and the 2015 ACM InternationalSymposium on Wearable Computers UbiComp and ISWC 2015pp 329ndash332 September 2015

[9] M Kurahashi K Murao T Terada and M Tsukamoto ldquoPer-sonal identification system based on rotation of toilet paperrollsrdquo in Proceedings of the IEEE International Conference onPervasive Computing and CommunicationsWorkshops (PerComWorkshops rsquo17) pp 521ndash526 IEEE Kona Hawaii USA March2017

[10] ldquoHapiforkrdquo httpswwwhapicom[11] Y Arakawa ldquoSenStick Sensorize every thingsrdquo in Proceedings of

the ACM International Joint Conference on Pervasive and Ubiq-uitous Computing and the 2015 ACM International SymposiumonWearable Computers UbiComp and ISWC 2015 pp 349ndash352September 2015

[12] Y Nakamura T Kanehira Y Arakawa and K YasumotoldquoSenStick 2 Ultra tiny all-in-one sensor with wireless chargingrdquoinProceedings of the 2016ACM International Joint Conference onPervasive and Ubiquitous Computing UbiComp 2016 pp 337ndash340 September 2016

[13] ldquoSensortag cc2541 (texas instruments)rdquo httpwwwticomtoolcc2541dk-sensor

[14] ldquoIot smart modulerdquo httpwwwalpscomjiotsmart[15] ldquoTsnd121 (atr-promotions)rdquo httpwwwatr-pcomproducts

TSND121html[16] ldquoWax9 (axivity)rdquo httpaxivitycomproductwax9[17] Uk biobank ldquoUk biobankrdquo httpwwwukbiobankacuk[18] J C Seidell and J Halberstadt ldquoThe global burden of obesity

and the challenges of preventionrdquo Annals of Nutrition andMetabolism vol 66 supplement 2 pp 7ndash12 2015

[19] ldquoWeltThe smart belt for fashion and healthrdquo httpswwwwelt-corpcom

[20] ldquoBelty power The belt your really deserverdquo httpswwwbeltyco

[21] H Nam J-H Kim and J-I Kim ldquoSmart Belt A wearabledevice for managing abdominal obesityrdquo in Proceedings of theInternational Conference on Big Data and Smart ComputingBigComp 2016 pp 430ndash434 January 2016

[22] K Moriya M Fujimoto Y Arakawa and K Yasumoto ldquoIndoorlocalization based on distance-illuminance model and activecontrol of lighting devicesrdquo in Proceedings of the 2016 Interna-tional Conference on Indoor Positioning and Indoor NavigationIPIN 2016 pp 1ndash6 October 2016

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal of

Volume 201

Submit your manuscripts athttpswwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

Page 16: SenStick: Comprehensive Sensing Platform with an Ultra ...downloads.hindawi.com/journals/js/2017/6308302.pdf · ResearchArticle SenStick: Comprehensive Sensing Platform with an Ultra

16 Journal of Sensors

References

[1] O Brdiczka M Langet J Maisonnasse and J L CrowleyldquoDetecting human behavior models from multimodal observa-tion in a smart homerdquo IEEE Transactions on Automation Scienceand Engineering vol 6 no 4 pp 588ndash597 2009

[2] J Chen A Kam J Zhang N Liu and L Shue ldquoBathroomactivity monitoring based on soundrdquo in Pervasive ComputingThird International Conference PERVASIVE 2005 MunichGermanyMay 8-13 2005 Proceedings vol 3468 ofLectureNotesin Computer Science pp 47ndash61 Springer Berlin Germany2002

[3] G Bauer K Stockinger and P Lukowicz ldquoRecognizing theUse-Mode of Kitchen Appliances from Their Current Consump-tionrdquo in Smart Sensing and Context vol 5741 of Lecture Notesin Computer Science pp 163ndash176 Springer Berlin HeidelbergBerlin Heidelberg 2009

[4] K Ouchi and M Doi ldquoIndoor-outdoor activity recognition bya smartphonerdquo in Proceedings of the the 2012 ACM Conferencep 537 Pittsburgh Pennsylvania September 2012

[5] A Anjum and M U Ilyas ldquoActivity recognition using smart-phone sensorsrdquo in Proceedings of the IEEE 10th ConsumerCommunications and Networking Conference (CCNC rsquo13) pp914ndash919 IEEE January 2013

[6] O D Lara and M A Labrador ldquoA survey on human activityrecognition using wearable sensorsrdquo IEEE CommunicationsSurveys amp Tutorials vol 15 no 3 pp 1192ndash1209 2013

[7] ldquoJins memerdquo httpsjins-memecom[8] Y Matsuda T Ishioka T Hasegawa et al ldquoWaistonBelt A belt

for monitoring your real abdominal circumference foreverrdquo inProceedings of the ACM International Joint Conference on Perva-sive and Ubiquitous Computing and the 2015 ACM InternationalSymposium on Wearable Computers UbiComp and ISWC 2015pp 329ndash332 September 2015

[9] M Kurahashi K Murao T Terada and M Tsukamoto ldquoPer-sonal identification system based on rotation of toilet paperrollsrdquo in Proceedings of the IEEE International Conference onPervasive Computing and CommunicationsWorkshops (PerComWorkshops rsquo17) pp 521ndash526 IEEE Kona Hawaii USA March2017

[10] ldquoHapiforkrdquo httpswwwhapicom[11] Y Arakawa ldquoSenStick Sensorize every thingsrdquo in Proceedings of

the ACM International Joint Conference on Pervasive and Ubiq-uitous Computing and the 2015 ACM International SymposiumonWearable Computers UbiComp and ISWC 2015 pp 349ndash352September 2015

[12] Y Nakamura T Kanehira Y Arakawa and K YasumotoldquoSenStick 2 Ultra tiny all-in-one sensor with wireless chargingrdquoinProceedings of the 2016ACM International Joint Conference onPervasive and Ubiquitous Computing UbiComp 2016 pp 337ndash340 September 2016

[13] ldquoSensortag cc2541 (texas instruments)rdquo httpwwwticomtoolcc2541dk-sensor

[14] ldquoIot smart modulerdquo httpwwwalpscomjiotsmart[15] ldquoTsnd121 (atr-promotions)rdquo httpwwwatr-pcomproducts

TSND121html[16] ldquoWax9 (axivity)rdquo httpaxivitycomproductwax9[17] Uk biobank ldquoUk biobankrdquo httpwwwukbiobankacuk[18] J C Seidell and J Halberstadt ldquoThe global burden of obesity

and the challenges of preventionrdquo Annals of Nutrition andMetabolism vol 66 supplement 2 pp 7ndash12 2015

[19] ldquoWeltThe smart belt for fashion and healthrdquo httpswwwwelt-corpcom

[20] ldquoBelty power The belt your really deserverdquo httpswwwbeltyco

[21] H Nam J-H Kim and J-I Kim ldquoSmart Belt A wearabledevice for managing abdominal obesityrdquo in Proceedings of theInternational Conference on Big Data and Smart ComputingBigComp 2016 pp 430ndash434 January 2016

[22] K Moriya M Fujimoto Y Arakawa and K Yasumoto ldquoIndoorlocalization based on distance-illuminance model and activecontrol of lighting devicesrdquo in Proceedings of the 2016 Interna-tional Conference on Indoor Positioning and Indoor NavigationIPIN 2016 pp 1ndash6 October 2016

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal of

Volume 201

Submit your manuscripts athttpswwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

Page 17: SenStick: Comprehensive Sensing Platform with an Ultra ...downloads.hindawi.com/journals/js/2017/6308302.pdf · ResearchArticle SenStick: Comprehensive Sensing Platform with an Ultra

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal of

Volume 201

Submit your manuscripts athttpswwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of