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...