Date of Award
Doctor of Philosophy in Electrical Engineering
Electrical, Computer, and Biomedical Engineering
According to limb loss statistics, there are over one million leg amputees in the US whose lives are severely impacted by their conditions. In order to improve the quality of life of patients with leg amputations, neural activities have been studied by many researchers for intuitive prosthesis control. The neural signals collected from muscles are electromyographic (EMG) signals, which represent neuromuscular activities and are effective bioelectrical signals for expressing movement intent. EMG pattern recognition (PR) is a widely used method for characterizing EMG signals and classifying movement intent. The key to the success of neural-controlled artificial limbs is the neural-machine interface (NMI) that collects neural signals, interprets the signals, and makes accurate decisions for prosthesis control.
This dissertation presents the design and implementation of a real-time NMI that recognizes user intent for control of artificial legs. To realize the NMI that can be carried by leg amputees in daily lives, a unique integration of the hardware and software of the NMI on an embedded system has been proposed, which is real-time, accurate, memory efficient, and reliable. The embedded NMI contains two major parts: a data collection module for sensing and buffering input signals and a computing engine for fast processing the user intent recognition (UIR) algorithm. The designed NMI has been completely built and tested as a working prototype. The system performance of the real-time experiments on both able-bodied and amputee subjects for recognizing multiple locomotion tasks has demonstrated the feasibility of a self-contained real-time NMI for artificial legs.
Zhang, Xiaorong, "On Design and Implementation of an Embedded System for Neural-Machine Interface for Artificial Legs" (2013). Open Access Dissertations. Paper 23.