Visible to the public CPS: Synergy: Sensor Network-Based Lower-Limb Prosthetic Optimization and ControlConflict Detection Enabled

Project Details
Lead PI:obai
Co-PI(s):Ding-Yu Fei
Zhixiu Han
Ashraf Gorgey
Douglas Murphy
Performance Period:12/01/14 - 10/31/15
Institution(s):Virginia Commonwealth University
Sponsor(s):National Science Foundation
Award Number:1446737
557 Reads. Placed 287 out of 803 NSF CPS Projects based on total reads on all related artifacts.
Abstract: More than one million people including many wounded warfighters from recent military missions are living with lower-limb amputation in the United States. This project will design wearable body area sensor systems for real-time measurement of amputee's energy expenditure and will develop computer algorithms for automatic lower-limb prosthesis optimization. The developed technology will offer a practical tool for the optimal prosthetic tuning that may maximally reduce amputee's energy expenditure during walking. Further, this project will develop user-control technology to support user's volitional control of lower-limb prostheses. The developed volitional control technology will allow the prosthesis to be adaptive to altered environments and situations such that amputees can walk as using their own biological limbs. An optimized prosthesis with user-control capability will increase equal force distribution on the intact and prosthetic limbs and decrease the risk of damage to the intact limb from the musculoskeletal imbalance or pathologies. Maintenance of health in these areas is essential for the amputee's quality of life and well-being. Student participation is supported. This research will advance Cyber-Physical Systems (CPS) science and engineering through the integration of sensor and computational technologies for the optimization and control of physical systems. This project will design body area sensor network systems which integrate spatiotemporal information from electromyography (EMG), electroencephalography (EEG) and inertia measurement unit (IMU) sensors, providing quantitative, real-time measurements of the user's physical load and mental effort for personalized prosthesis optimization. This project will design machine learning technology-based, automatic prosthesis parameter optimization technology to support in-home prosthesis optimization by users themselves. This project will also develop an EEG-based, embedded computing-supported volitional control technology to support user?s volitional control of a prosthesis in real-time by their thoughts to cope with altered situations and environments. The technical advances from this project will provide wearable and wireless body area sensing solutions for broader applications in healthcare and human-CPS interaction applications. The explored computational methods will be broadly applicable for real-time, automatic target recognition from spatiotemporal, multivariate data in CPS-related communication and control applications. This synergic project will be implemented under multidisciplinary team collaboration among computer scientists and engineers, clinicians and prosthetic industry engineers. This project will also provide interdisciplinary, CPS relevant training for both undergraduate and graduate students by integrating computational methods with sensor network, embedded processors, human physical and mental activity recognition, and prosthetic control.