Classification of Finger Movements for Prosthesis Control with Surface Electromyography

Abstract

Surface electromyography (sEMG) signals can be used in the medical, rehabilitation, robotics, and industrial fields. In this paper, we assess a method of classifying finger movements for dexterous prosthetic hand control. The sEMG signals from five volunteers are recorded, and then pattern recognition is carried out by data preprocessing, feature extraction, and classification. The results show that high recognition accuracy can be achieved by time domain feature extraction and the use of an artificial neural network. To find the tradeoff between the number of channels and the recognition accuracy, the number of channels is reduced, and it is found that the minimum number of channels required for high accuracy is seven, giving a recognition accuracy of 90.52%.