Project Title

CSR:EAGER: A Wearable Body Motion Sensing Platform Using Conductive Stretchable Fabric

Project Description

This project aims to develop a wearable platform that enables the generation of a three dimensional model of the human skeleton and tracking its motion. There exist techniques that use depth cameras and special image processing software to model the motion of the human skeleton, and Kinect is a well-known example. However, the techniques using fixed cameras need to deploy camera devices at fixed locations in order to sense human motions. While this is ok for indoor fixed scenarios, it does not work for outdoor ubiquitous scenarios. Instead, the wearable platform from this project works for both scenarios.

This wearable platform is designed using conductive stretchable fabric that is comfortable to wear. This project exploits the fabric’s resistance change for inferring the bend angle of a body joint that the fabric covers. Joints’ angles are then fused with body skeleton to estimate 3D body motions. The main intellectual merits include a novel wearable platform that enables ubiquitous body motion sensing, novel user-dependent angle detection sensors with nonlinear mapping models from resistance measurements to joint bend angles, and a novel 3D body motion estimation method that fuses independent joint angles and optimizes estimation results with body kinematics constraints.

Personnel

1. Gang Zhou, faculty
2. Amanda Watson, Ph.D. student, woman student
3. Minglong Sun, Ph.D. student
4. Woosub Jung, Ph.D. student
5. Shuangquan Wang, Ph.D. student
6. Yongsen Ma, Ph.D. student
7. Kenneth Koltermann, Ph.D. student
8. Qihan Wang, Ph.D. student in Zhou’s class, woman student
9. Samhita Pendyal, undergraduate student, woman student
10. Andrew lyubovsky, (REU) undergraduate student
11. Luke McDevitte, REU undergraduate student from Brown University

Publications

1. Building a Skeleton-based 3D Body Model with Angle Sensor Data, Qihan Wang, Gang Zhou, Zhenming Liu, Bin Ren, Elsevier Smart Health, 2020 (PDF)
2. Wearable Computing of Freezing of Gait in Parkinson’s Disease: A Survey, Minglong Sun, Amanda Watson, Gang Zhou, Elsevier Smart Health, 2020 (PDF)
3. Location and Person Independent Activity Recognition with WiFi, Deep Neural Networks and Reinforcement Learning, Yongsen Ma, Sheheryar Arshad, Swetha Muniraju, Eric Torkildson, Enrico Rantala, Klaus Doppler, Gang Zhou, ACM Transactions on Internet of Things, 2020 (PDF)
4. TracKnee: Knee Angle Measurement Using Stretchable Conductive Fabric Sensors, Amanda Watson, Minglong Sun, Samhita Pendyal, Gang Zhou, ACM/IEEE Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), Washington D.C., 2019 (PDF)

TracKnee Sensor Prototype using Stretchable Conductive Fabric