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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. One of the challenges for applying the designed PR-based NMI to clinical practice is the lack of practical system training methods. The traditional training procedure for the locomotion mode recognition (LMR) system is time consuming and manually conducted by experts. To address this challenge, an automatic and userdriven training method for the LMR system has been presented in this dissertation. In this method, a wearable terrain detection interface based on a portable laser distance sensor and an inertial measurement unit is applied to detect the terrain change in front of the prosthesis user. The identification of terrain alterations together with the information of current gait phase can be used to automatically identify the transitions among various locomotion modes, and labels the training data with movement class in real-time. The pilot experimental results on an able-bodied subject have demonstrated that this new method can significantly simplify the LMR training system and the training procedure without sacrificing the system performance. Environmental uncertainty is another challenge to the design of NMI for artificial limbs. EMG signals can be easily contaminated by noise and disturbances, which may degrade the classification performance. The last part of the dissertation presents a realtime implementation of a self-recovery EMG PR interface. A novel self-recovery module consisting of multiple sensor fault detectors and a fast linear discriminant analysis (LDA) based classifier retraining strategy has been developed to immediately recover the classification performance from signal disturbances. The self-recovery EMG PR system has been implemented on a real-time embedded system. The preliminary experimental evaluation on an able-bodied subject has shown that the system can maintain high accuracy in classifying multiple movement tasks while motion artifacts have been manually introduced. The results may propel the clinical use of EMG PR for multifunctional prosthesis control.

On Design and Implementation of an Embedded System for Neural-Machine Interface for Artificial Legs