Classification of 41 Hand and Wrist Movements via Surface Electromyogram Using Deep Neural Network.

ABSTRACT: Surface electromyography (sEMG) is a non-invasive and straightforward way to allow the user to actively control the prosthesis. However, results reported by previous studies on using sEMG for hand and wrist movement classification vary by a large margin, due to several factors including but not limited to the number of classes and the acquisition protocol. The objective of this paper is to investigate the deep neural network approach on the classification of 41 hand and wrist movements based on the sEMG signal. The proposed models were trained and evaluated using the publicly available database from the Ninapro project, one of the largest public sEMG databases for advanced hand myoelectric prosthetics. Two datasets, DB5 with a low-cost 16 channels and 200 Hz sampling rate setup and DB7 wit

SUBMITTER: Sri-Iesaranusorn P 

PROVIDER: S-EPMC8220079 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature