Improved sEMG signal classification using the twin SVM

Identifying wrist and finger flexions from surface Electromyogram (sEMG) signals finds several applications for developing prosthesis-based device control. However, sEMG signals can be corrupted by muscular activity from multiple sources at the site of acquisition, and hence the identification of intents from these signals presents a challenge. Moreover, there can be multiple intents which need to be recognized, hence a robust classifier is required. The accurate recognition of these movements is imperative as it enables reliable control of devices. In this paper, we use the Twin Support Vector Machine (Twin SVM) classifier to identify 15 classes of wrist and finger flexions using one-v/srest classification approach. Our work uses sEMG data obtained from nine subjects, including an amputee volunteer. We compare the improved accuracy obtained in using Twin SVM against LIBSVM (a standard SVM implementation) to demonstrate the effectiveness of the classifier. We use a simple feature - the Root Mean Square (RMS) value of the signal during the trial as features for the classifier. Our results demonstrate the effectiveness of using the Twin SVM in a multi-class scenario with unbalanced datasets, which holds significance in addressing the broader challenges in classification presented in several applications based on processing of biomedical signals.