A performance comparison of hand motion EMG classification

Abstract

Powered prosthesis is of considerable value to amputees to enable them to perform their daily-life activities with convenience. One of applicable control signals for controlling a powered prosthesis is the myoelectric signal. A number of commercial products have been developed that utilize myoelectric control for powered prostheses; however, the functionality of these devices is still insufficient to satisfy the needs of amputees. For the purpose of a comparison, several electromyogram classification methods have been studied in this paper. The performance criteria included not only classification accuracy, but also repeatability and robustness of the classifier, training time for online training performance, and computational time for real-time operation were evaluated with seven classification algorithms. The study included five different feature sets with time-domain feature values and autoregressive model coefficients. In summary, the quadratic discriminant analysis showed a remarkable performance in terms of high classification accuracy, high robustness, and low computational time of training and classification from the experiment results.