An E-Textiles

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 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 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