Integrating neuromuscular and cyber systems for neural control of artificial legs
Date of Original Version
This paper presents a design and implementation of a cyber-physical system (CPS) for neurally controlled artificial legs. The key to the new CPS system is the neural-machine interface (NMI) that uses an embedded computer to collect and interpret electromyographic (EMG) signals from a physical system that is a leg amputee. A new deciphering algorithm, composed of an EMG pattern classifier and finite state machine (FSM), was developed to identify the user's intended lower limb movements. To deal with environmental uncertainty, a trust management mechanism was designed to handle unexpected sensor failures and signal disturbances. Integrating the neural deciphering algorithm with the trust management mechanism resulted in a highly accurate and reliable software system for neural control of artificial legs. The software was then embedded in a newly designed hardware platform based on an embedded microcontroller and a graphic processing unit (GPU) to form a complete NMI for real time testing. Our preliminary experiment on a human subject demonstrated the feasibility of our designed real-time neural-machine interface for artificial legs. © 2010 ACM.
Proceedings of the 1st ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS '10
Huang, He, Yan Sun, Qing Yang, Fan Zhang, Xiaorong Zhang, Yuhong Liu, Jin Ren, and Fabian Sierra. "Integrating neuromuscular and cyber systems for neural control of artificial legs." Proceedings of the 1st ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS '10 , (2010): 129-138. doi:10.1145/1795194.1795213.