An E-Textile System for Motion Analysis

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

2. 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 Virginia Tech e-Textiles Group

3. 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 Virginia Tech e-Textiles Group

4. 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 Virginia Tech e-Textiles Group

5. 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 Virginia Tech e-Textiles Group

6. 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 Virginia Tech e-Textiles Group

7. 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? Virginia Tech e-Textiles Group

8. 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 Virginia Tech e-Textiles Group

9. 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 Virginia Tech e-Textiles Group

10. Simulation Stream Lab-recorded video from actual subjects Simulation model of sensors based on body position data Extraction of body position information Prototype Data Acquisition Sensor input from subject wearing e-textile garment Dependent Measure Extraction Module Input: Real or simulated sensor time series Output: Dependent measures such as acceleration, angular velocity, total energy Activity Classification Module Input: Dependent measures of body actions Output: Classification of activity into categories such as walking, running, or sitting Exploring the Design Space Through Simulation Virginia Tech e-Textiles Group

11. 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 Virginia Tech e-Textiles Group

12. 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 Virginia Tech e-Textiles Group

13. 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 Virginia Tech e-Textiles Group