A Machine Learning System for Classification of EMG Signals to Assist Exoskeleton Performance

Abstract

A surface electromyographic signal can provide information on neuromuscular activity and can be used as an input in a myoelectric control system for applications such as orthotic exoskeletons. In this process, a key step is to extract useful information from the EMG signals using the pattern recognition tools. Our research focus is on identification of a set of relevant features for efficient EMG signal classification. Specifically in this work, from the pre-processed myoelectric signals, we extracted auto regression coefficients, different time-domain features such as Hjorth features, integral absolute value, mean absolute value, root mean square and cepstral features. Next a subset consisting of a few selected features are fed to the multiclass SVM classifier. Using a radial basis function kernel a classification accuracy of 92.3% has been achieved.