skinput

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Nikitha VidyadharNO : 32

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Can You Ever Can You Ever Imagine Imagine

Human Body Human Body As AAs A

TOUCH TOUCH SCREEN?????SCREEN?????

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YEAH !!!YEAH !!!

THERE IS A SOLUTIONTHERE IS A SOLUTION AND AND

THAT IS THAT IS

SKINPUTSKINPUT

STRUCTURE OF PRESENTATIONSTRUCTURE OF PRESENTATION

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Motivation

Overview Principle of Skinput

Mechanism and Processing model

Experiment

Advantages And Disadvantages

Skinput in future

Conclusion

MOTIVATIONMOTIVATION

Touch screens are familiar nowadays.Certain drawbacks of them are : Limited interaction space

Less accurate interactions

Cannot make buttons and screens larger without losing the primary benefits of small size

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OVERVIEWOVERVIEWGiving input through skin.

Skinput turns human body into touch screen input surface.

Developed by Chris Harrison, Desney Tan, and

Dan Morris of the Microsoft Research's

Computational User Experiences Group. Its first public appearance was at Microsoft's

Tech Fest  2010

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WHAT SKINPUT DOESWHAT SKINPUT DOES??Primary functions areControl audio devices.Make phone calls.Navigate browsing systems.Play games.

Uses series of sensors to determine where user tapped on skin

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PRINCIPLE OF SKINPUTPRINCIPLE OF SKINPUT

It listens to vibrations in your body.

Skinput also responds to the various hand gestures.

The arm is an instrument

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WHAT MAKES IT WORKWHAT MAKES IT WORK??

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PICO PROJECTORPICO PROJECTOR

It is also known as pocket projector or mobile projector. Displays Menu. The system comprises three main parts:

The Laser light source The Combiner optics The Scanning mirror

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BIO ACOUSTICSBIO ACOUSTICS

Study of sound waves inside living body.

When a finger taps the skin, several distinct forms of acoustic energy are produced.

Longitudinal WavesTransverse Waves

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TRANSVERSE WAVESTRANSVERSE WAVESCreated by the displacement of the skin

from a finger impact.

They are visible when we slow down it 14 times.

Tapping on soft regions of the arm create higher amplitude transverse wave than tapping on boney areas.

Sensors activated as wave passes under it. 04/12/23SKINPUT 12

LONGITUDINAL WAVESLONGITUDINAL WAVESSome of the energy is transmitted inwards

towards the Skelton.

Causes internal skeletal structure to vibrate.

These waves travel through the soft tissues of the arms, exciting the bone .

It responds to mechanical excitation by rotating rigid body.

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BIO ACOUSTICS : BIO ACOUSTICS : SENSINGSENSING

Each body part creates different type of vibrations

depending on:BonesMusclesTendons

These signal need to be sensed and worked upon.

This is done by wearing wave sensor armbands.

Sensing elements detect vibrations transmitted through the body.

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ARMBANDARMBAND

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INSIDE VIEWINSIDE VIEW

OUTSIDE VIEW

OUTSIDE VIEW

ARMBAND ARMBAND MECHANISMMECHANISM

Detect vibrations transmitted through the body.

Arm band device consists of two components: 1. Projector 2. Detector

Armbands are placed in 2 ways.Above the elbow Below the elbow

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ARMBAND PROTYPEARMBAND PROTYPE

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Two arrays of 5 sensing elements incorporated into an armband.

2 Sensor packages focus on the arm of armband

One package was located near the Radius other near the Ulna.

BLUETOOTHBLUETOOTH

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Armband is connected to Mobile Device or Computer via Bluetooth

MECHANISMMECHANISM

Arm band  detects the acoustic signals and convert them to electronic signals which

easily enable the users to perform simple tasks as

browsing through a mobile phone menu,

making calls, controlling portable music players etc.

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PROCESSINGPROCESSING MODELMODEL

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Projector display image

on arm

Vibrations are captured from

sensors

This is connected to Mobile Device via Bluetooth.

A software to match sound

frequencies to specific skin location is

used.

Corresponding action is

implemented in device

Finger taps on arm

PROCESSING(PROCESSING(detaileddetailed))

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Employ a Mackie Onyx 1200F audio interface to

digitally capture data from ten sensors.

Connected via Fire Wire to a conventional

computer

Each channel was sampled at 5.5

KHZ(55KHZ total)by

ATmega168 processor

Data was sent to our primary application

written in JAVA

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This program perform three key functions:-

Provides a live visualization of the data

It segments inputs from the data stream into independent instances(taps).

Classified these input instances

The audio stream was segmented into individual taps using an absolute exponential average of all 10 channels

SEGMENTATIONSEGMENTATION

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After the input has been segmented ,the wave forms are analyzed.

Employing a brute force learning approach computing 186 features in total( includes combinationarily).

186 features includes

Average amplitude, SD and total energy of the waveforms in each channel(30 features).

Average amplitude ratios b/w channel pairs(45 features).

Average of these ratios(1 feature). 256 point FFT for all 10 channels (take only lower 10

values ie (100 features). a rough estimation of the fundamental frequency of the

signal displacing each sensor (10 features).

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BRUTE FORCE LEARNING approach computes 186

features in total.

Support Vector Machine

Classify Input instances using Weka Machine Learning Tool

Kit.

Event associated with location is instantiated.

Interactive feature bounded to that event fired.

EXPERIMENTEXPERIMENT

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Participants 13 ->7 female, 6 male. Ages ranged from 20 to 56. Body mass indexes (BMIs) ranged

from 20.5 (normal) to 31.9 (obese).

Three input groupings from the multitude of possible location combinations to test .

From these groupings,5 experimental conditions was derived.

LOCATIONSLOCATIONS

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TRAINING AND CLASSIFICATIONTRAINING AND CLASSIFICATION

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RESULTSRESULTSFIVE FINGERSFIVE FINGERS

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Accuracy remained high for five finger

condition averaging 87.7%

When classification was incorrect, the system believed the input to be an adjacent finger 60.5% of the time.

Ring finger constituted 63.3% of the misclassifications.

WHOLE ARMWHOLE ARM

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Below-elbow placement performed the best posting at 95.5% accuracy.

Moving the sensor above the elbow reduced accuracy to 88.3 (7.2% drop).

The eyes free input condition yielded lower accuracies than other conditions averaging

85%(10.5% drop)

FOREARMFOREARM

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Classification accuracy for the 10 location forearm condition stood at 81.5%.

Higher accuracies can be achieved by collapsing the 10 locations into groups.

BMI EFFECTSBMI EFFECTS

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High BMI is correlated with decreased accuracies.

No direct relation with gender of the participants

WALKING AND JOGGINGWALKING AND JOGGING

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Each participant trained and tested the system while walking and jogging on a treadmill.

3 input location were used to evaluate accuracy .

In walking trials system never produced a false input.

In jogging trials the system had 4 false-positive input events over 6 min of continuous jogging.

Accuracy decreased to 83.3% and 60% for male and female participants respectively.

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ADVANTAGESADVANTAGES No need to interact with the gadget directly.

Don’t have to worry about keypad.

People with larger fingers get trouble in navigating tiny buttons and keypads on mobile phones. With skin put this problem disappears.

The body is portable and always available, and fingers are a natural input device.

DISADVANTAGESDISADVANTAGES

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Band seems easy enough to slip on, people found difficult to wear a very big band around their arm for the day.

Not enough research has been conducted product to test the possible skin diseases get from using this product.

Very high cost which will not be affordable for the common man.

SKINPUT IN FUTURESKINPUT IN FUTURE

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A person might walk toward their home, tap their palm to unlock the door and then tap some virtual buttons on their arms to turn on the TV and start flipping through channels.

Extensive Research is going on Currently on Skinput to

make the armband more smaller.Incorporate More Devices with This System.Extend accuracy level.

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With small sized Pico-projectors ,Skinput oriented systems are emerging trend.

Research carried out for smaller wrist band watch sized sensor armband.

FUTURE IMPLICATIONS

CONCLUSIONCONCLUSION

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Skinput allows the human body as an input surface.

It describes a novel, wearable bio-acoustic sensing array that we built into an armband in order to detect and localize finger taps on the forearm and hand.

We conclude with descriptions of several prototype applications that demonstrate the rich design space we believe Skinput enables.

BIBLIOGRAPHYBIBLIOGRAPHY

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http://research.microsoft.com/enus/um/redmond/groups/cue/skinput Official Home Page of Skinput.

http://www.chi2010.org Home Page of Computer & Human Interactions Conference, April,2010.

http://www.chrisharrison.net/projects/skinput Personal Homepage of Chris Harrison.

http://research.microsoft.com/en-us/um/people/dan Homepage of Dan Morris.

SKINPUT: Appropriating the Body as an Input Surface Chris Harrison , Desney Tan , Dan Morris

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