Feature Selection and Ranking in EMG Analysis for Hand Movement Classification

Abstract

Surface Electromyography has gained tremendous significance in the recent years due to its suitability and reliability in a wide range of applications like automatic prosthetic control, diagnosis of neuromuscular disorders, in robotics and many such fields. Considering such applications, identification of various muscular movements is necessary and hence, EMG pattern recognition is needed. This paper focusses on a generalised EMG pattern recognition of various hand movements. The data from Ninapro Database - 4 has been used for pattern recognition. The database has Surface Electromyogram (sEMG) data of 52 various hand movements. The data was subjected to pre-processing, feature extraction and classification. An average accuracy of 64.87% was obtained for a combination of seven features in the time (temporal) domain, using Linear Discriminant Analysis (LDA) as the classification model. The obtained classification accuracies are compared and discussed with respect to the state-of-the-art literature. © 2023 IEEE.