Muscle Activity Distribution Features Extracted from HD sEMG to Perform Forearm Pattern Recognition

Abstract

An efficient pattern recognition system based exclusively on forearm surface Electromyographic (sEMG) signals is proposed to provide a more intuitive control of a robotic arm used by some of the disabled. The main contribution of this paper is the use of an original set of features characterizing the muscle activity distribution obtained with high-density sEMG (HD sEMG) sensors. Contrary to simple sEMG, HD sEMG can produce muscle activity images with spatial distributions that differ according to forearm movement. In order to translate this distribution, the proposed set of features includes the center of gravity, the mean amplitude and the percentage of influence computed in each HD sEMG image divided in sub-images. Based on these features, the recognition system locates nine forearm movements with high classification accuracies (99.23%). The results in terms of the number of learning data, the image resolutions (spatial filtering) and the number of sub-images demonstrate the potential of the proposed recognition system and its good performance-complexity trade-off.