Integrating neuromuscular and cyber systems for neural control of artificial legs

Abstract

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.