Improved sEMG signal classification using the Twin SVM
Published: Oct 1, 2016
Abstract
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...
Paper Details
Title
Improved sEMG signal classification using the Twin SVM
Published Date
Oct 1, 2016
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