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