EHealth Workshop 2003Virginia Tech e-Textiles Group An E-Textile System for Motion Analysis Mark Jones, Thurmon Lockhart, and Thomas Martin Virginia Tech

eHealth Workshop 2003 Virginia Tech e-Textiles Group

An E-Textile System for Motion Analysis

Mark Jones, Thurmon Lockhart, and Thomas MartinVirginia Tech


Virginia Tech e-Textiles Group

Design of an e-textile computer architecture

– Networking– Fault tolerance– Power aware– Programming model

Design through simulation– Emulation/Simulation

environment– Across population

Development of application prototypes


Application Motivation

Falls are one of the leading causes of death among the elderly in the U.S.

– Only 50% of those hospitalized with fall-related injuries survive their next year

– “Hip pads” for at-risk patients are bulky and inconvenient, leading to low compliance rates

E-textiles have been shown to have significant potential in the health care field

– Our goal is to develop an e-textile solution that will achieve high compliance rates


Gait Analysis

Gait analysis can identify patients at risk for falling as well as several pathological conditions

Currently performed in dedicated laboratories at high expense

– Somewhat artificial– Time consuming Virginia Tech Locomotion Laboratory


Measures in Gait Analysis

Raw Data– Position (x,y,z) of the

body – Force of foot-to-ground

Gait measures– Stride length– Required coefficient of

friction– Transition of center of

mass– Width of gait


E-Textile for Gait Analysis

We are building an e-textile system with the following features:

– Pants augmented with sensors– Footwear with two force sensors– Hip airbag for the pants– Remote communication device

Advantages: no time for setup, can be used in home environment, mitigates fall impact, users more likely to be compliant, more natural measurements

The design issues identified are discussed in the following slides


How to Obtain Gait Measures

The sensors under consideration (accelerometers, force sensors, angular velocity sensors, gyroscopes) do not directly sense any of the gait measures

We propose that a combination of sensors, combined with computation, can determine these gait measures

Design Issue: What is the set of sensors that will provide these measures at an acceptable accuracy level?


Designing for the Masses

The proposed system must work across a range of sizes and gait types

– A single weave design for the bolts of cloth– Standard garment sizes constructed from that bolt of cloth

Sensors will be in slightly different positions on each user due to motion and size differences

Range of sensor readings will vary across users Design Issue: It is not practical to assume that we

can construct and test prototypes for a range of users repeatedly while exploring the design space


Application Functionality

What is required to provide informative data?– In the gait analysis laboratory, the system is only triggered

for a brief period of time as the user is in the correct location and walking

In a doctor’s office, we need to record and analyze data only during a specified period

– Avoid time-consuming data searching In a home setting we need more automation

– Must identify when a user is walking, then trigger recording– Must identify when a user is falling, then trigger air bag


Exploring the Design Space Through Simulation

Sensor input from subject wearing e-textile garment

Prototype Data Acquisition Dependent Measure Extraction ModuleInput: Real or simulated sensor time seriesOutput: Dependent measures such as acceleration, angular velocity, total energy

Activity Classification ModuleInput: Dependent measures of body actionsOutput: Classification of activity into categories such as walking, running, or sitting

Lab-recorded video from actual subjects

Extraction of body position information

Simulation model of sensors based on body position data

Simulation Stream


Current Status of Garment

We have fabricated a pair of pants for motion classification

– Designed through simulation– Trained neural network across a

range of virtual users Tested the pants successfully on

the first “real” wearer– Worked with NN trained via virtual

users Features of our architecture

– All digital communication– Fault tolerant– Power aware operation– On-garment computation and

decision making


Computing Gait Measures:Stride Length Example

Accelerometer on each ankle– Identify begin/end of stride in the data (force sensors will be

used for more accuracy later)– Integrate the acceleration value twice to find the distance

traveled by the ankle

Gait analysis studies provide us with the data to determine what is significant error

– For example, we can use the mean heel velocity in two subject groups as well as the standard deviation of heel velocity


Conclusions and Future Directions

E-textiles hold great promise in improving the usability and acceptance of home health care devices

– Cross-disciplinary teams are essential Design for cost-effective fabrication may allow for

wider spread adoption– Simulation can be very effective in the design process– Common architecture can speed design and deployment

Gait analysis is an area where early impact of e-textiles is possible

– Evaluation and deployment plan is essential

Documents

EHealth Workshop 2003Virginia Tech e-Textiles Group An E-Textile System for Motion Analysis Mark Jones, Thurmon Lockhart, and Thomas Martin Virginia Tech