Analysis and Comparison of Features and Algorithms to Classify Shoulder Movements From sEMG Signals

Abstract

Shoulder movements are not considered for electromyography-based pattern classification control, due to the difficulty to manufacture three-degrees-of-freedom shoulder prostheses. This paper aims at exploring the feasibility of classifying up to nine shoulder movements by processing surface electromyography signals from eight trunk muscles. Experimenting with different pattern recognition methods, two classifiers were developed, considering six different combinations of window sizes and increments, and three feature sets for each channel. Applying linear discriminant analysis the best performance was obtained on a window length of 500 ms associated to temporal increments of 62 ms. This setting yielded a 100% accuracy for recognizing four movements, and progressively degraded to 92% for nine movements. Using neural networks, higher accuracy was obtained in particular in the 9-class problem. Finally, the signals from the eight channels were analyzed in order to check the possibility to reduce the number of acquisition channels.