Spring 09 Priti Aghera

CSE 237D paghera@cs.ucsd.edu

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Context Aware Healthcare Application

Background: Wireless communications technology has become readily available for the general

public in recent years. The new generation of wireless mobile systems with seamless

integration of 2.5G and 3G cellular systems, wireless LAN, Bluetooth, and Zigbee can provide

wide coverage and an improved capacity to run many different types of wireless applications

including healthcare applications. In addition, advances in integrated circuit design and

bioengineering have led to the design of low-cost, miniature, lightweight, physiological sensors

that can be seamlessly integrated into a body area network for human health monitoring i.e.

wireless healthcare. Wireless healthcare- Wireless enabled Healthcare to anyone, anywhere

and anytime by removing location, time and other restraints while increasing both coverage

and the quality of the health services.

There are various applications of wireless healthcare in the medical field which fall into one

of these two categories: i) disease management, ii) assisted living for the elderly. CardioNet 3 is

one of the commercial disease management applications which provides 24 x 7 cardiac

monitoring service with beat-to-beat, real time analysis, automatic arrhythmia detection and

wireless ECG transmission. The Mobihealth project in Europe [7] is one of the biggest example

of industrial and academic collaboration for research on wireless healthcare. MobiHealth is a

mobile healthcare project funded by the European Commission. The MobiHealth consortium

unites 14 partners from five European countries and represents all the relevant disciplines.

Partners include: hospitals and medical service providers, universities, mobile network

operators, mobile application service providers and mobile infrastructure and hardware

suppliers. The MobiHealth system allows patients to be fully mobile whilst undergoing health

monitoring. The patients wear a lightweight monitoring system - the MobiHealth BAN (Body

Area Network) - which is customized to their individual health needs. There are many systems

like Alarm-Net [4], I-Living5, and PAMM 6 to aid the elderly in their day to day life. Smartcane is

a cane enabled with different sensors like accelerometer and gyrometers and wireless radios to

detect a fall and automatically call emergency services or relatives of the elderly person.

The above mentioned projects are for subjects having some kind of health problems and to

monitor their vital signs continuously. On the other hand this project will focus on the third

area of wireless healthcare i.e. preventive healthcare. This project focuses on behavioral

change of subjects by motivating them and reminding them of their goals. People nowadays are

more aware of their health and eating habits because sedentary lifestyle and poor diet are the

leading cause of obesity and type II diabetes amongst adolescents in world. Controlling obesity

requires combination of good nutrition plan and physical activity plan. The biggest difficulty

patients find is adhering to the food and physical activity regime and over the period of time

they lose the motivation to continue. In this project I address this problem by utilizing the

ubiquitous wireless connectivity along with the advances in sensing and localization

technologies (e.g. accelerometers, GPS etc…).

Spring 09 Priti Aghera

CSE 237D paghera@cs.ucsd.edu

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Project Goal:

Advances in mobile and ubiquitous computing, wireless communications, mobile positioning

and sensor technologies have given a rise to a new class of mobile and ubiquitous applications

and services that are aware of the context of the application usage. Context-aware applications

are able to modify and adapt their behavior, operations and interface to best meet the user’s

current and continuously changing context without explicit user intervention. Context

represents any information that can be used to characterize the situation of entities that are

considered relevant to the interaction between a user and an application, such as the user

location, his preferences, his activities, nearby people and objects, the environmental

conditions, the availability of computing and communication resources etc. Context-aware

applications provide enhanced usability, minimize distraction and needed attention of the user

and can be used in various application domains, such as smart homes, wireless healthcare,

disaster management, etc. One of the active areas of research in mobile healthcare is context

aware intervention for preventive healthcare. Such applications are used for behavioral change

in preventive medicine to promote physical activity and healthy nutrition with adolescents and

adults. The goal of this project is to develop an energy efficient context-aware healthcare

application for behavioral change in subjects using location and sensor data as context. We

plan to provide context (location, time, physical activity, history of actions) aware prompts to

user using her mobile phone to modify health behavior like food intake and physical activity.

Ref[1] showed that message based intervention helped participants to achieve 3.16% weight

loss. This project is different from [1] as it adds a knowledge layer by using the context

information from the GPS and accelerometer sensors. Apple has many applications like “lose it”

and “Calorie Tracker”, “Restaurant Nutrition” to track you calorie intake in order to control

user’s weight. But it depends on the user to input the amount of workout he/she did while this

system will figure it out based on the sensor data.

There are several challenges in designing a mobile healthcare application. One of the challenges

is the limited battery life of mobile phones and sensors. For example, if GPS receiver is turned

on all the time, phone’s battery life is significantly reduced. We would like to make use of user’s

context to disable power consuming operations of the application. Context can be used to

choose a different duty cycling (sample more/less frequently) for a set of sensor devices. For

example in health monitoring application, PAEE (physical activity energy expenditure) sensors

can sample more frequently when the user is actively running and sample less frequently when

the user is idle(sleeping). Similarly, the decision of when to send data to backend or local server

can be context aware as well. For example, send data immediately in case of emergency and

don’t send if not critical and can be sent in future. This objective would be secondary during

this project as there are many integration challenges like, making MyExperience software work

on the phone, configuring MSP to run user activity inference daemon, integrating MSP and

phone via bluetooth communication etc… to be overcome before we can optimize the battery

life.

Spring 09 Priti Aghera

CSE 237D paghera@cs.ucsd.edu

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Approach:

I will build a context aware mobile application that enables health related behavioral changes in

user by providing useful prompts. Following is a list of such prompts.

i. Will note the working hours of users and will prompt to go to gym after work or in the

morning

ii. Keep track of steps walked by the user during the day and prompt them to go for walk

when the count is less than average.

iii. Will detect if the user is sedentary for a long time and encourage them for a workout.

iv. Mobile will prompt the user to buy vegetables, fruits and low calorie food when at

grocery store.

v. Will prompt the user to go for walk/exercise when they are near a park or YMCA after

work

vi. When in a food court will prompt user to try healthier menu items, or try a different

restaurant

The system has a three tier architecture. Various sensors or one device with many sensing

capabilities carried by the user forms the first tier. A smartphone acts as a local

aggregator/server for the system. The third tier would be a backend server. The phone would

detect the context and prompt the user. The phone will make use of “My Experience” tool [2]

developed by Intel. The sensor platform will communicate with the smartphone via Bluetooth

and the mobile will communicate using either Wi-Fi or Cellular network.

I will use a HTC diamond 3G phone [9] on Sprint network as a primary user device. Phone has a

GPS receiver acting as a location sensor. User will also carry an Intel’s MSP sensor [8] which

contains accelerometer and will be used to derive a user activity state

(walking/standing/running).

Project Milestones:

1. Define a doable scope for the project ------> Done

2. Perform a literature survey for similar work ------> Done

Spring 09 Priti Aghera

CSE 237D paghera@cs.ucsd.edu

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3. Select the components to be used in the system ------> Done

4. Get familiar with the My Experience data intervention tool ------> Current

5. Learn how to grab a fix for GPS location ------> To be done

6. Get familiar with the MSP sensor and learn how to program it ------> To be done

7. Make the MSP communicate with the phone. ------> To be done

8. Identify data sources for location specific information (food places, gym, sports facility,

etc…) and use them to build the location specific context of a user. ------> To be done

9. Develop a context aware algorithm/engine which uses collected context information

and history to generate appropriate prompts and record user’s responses. This

component will also send data to server. ------> To be done

10. If time permits vary the sampling frequency of the sensors to manage energy

intelligently. ------> To be done

References:

1. Kevin Patrick, Fred Raab, Marc A. Adams, Lindsay Dillon, Marion Zabinski, Cheryl Rock, William G.

Griswold, Gregory J. Norman J Med Internet Res 2009 (Jan 13): A Text Message-Based Intervention for

Weight Loss: Randomized Controlled Trial

2. “My Experience” tool: http://myexperience.sourceforge.net/ 3. CardioNet, http://www.cardionet.com/

4. A. Wood, G. Virone, T. Doan, Q. Cao, L. Selavo, Y. Wu, L. Fang, Z. He, S. Lin, J. Stankovic, “ALARM-

NET: Wireless Sensor Networks for Assisted-Living and Residential Monitoring”, Technical Report CS-

2006-13 Department of Computer Science, University of Virginia

5. Qixin Wang, Wook Shin, Xue Liu, Zheng Zeng, Cham Oh, Bedoor K. AlShebli, Marco Caccamo, Carl A.

Gunter, Elsa Gunter, Jennifer Hou, Karrie Karahalios, and Lui Sha, “I-Living: An Open System

Architecture for Assisted Living”,

6. Hamid Aghajan, Juan Carlos Augusto, Chen Wu, Paul McCullagh, and Julie-Ann Walkden, “Distributed

Vision-Based Accident Management for Assisted Living”\

7. Mobihealth Project: http://www.mobihealth.org/

8. MSP: http://seattle.intel-research.net/MSP/

9. HTC Diamond Smartphone: http://reviews.cnet.com/smartphones/htc-touch-diamond-sprint/4505-6452_7-33238694.html