SEMG-Based Gesture Classifier for a Rehabilitation Glove.

ABSTRACT: Human hand gesture recognition from surface electromyography (sEMG) signals is one of the main paradigms for prosthetic and rehabilitation device control. The accuracy of gesture recognition is correlated with the control mechanism. In this work, a new classifier based on the Bayesian neural network, pattern recognition networks, and layer recurrent network is presented. The online results obtained with this architecture represent a promising solution for hand gesture recognition (98.7% accuracy) in sEMG signal classification. For real time classification performance with rehabilitation devices, a new simple and efficient interface is developed in which users can re-train the classification algorithm with their own sEMG gesture data in a few minutes while enables shape memory alloy-based r

SUBMITTER: Copaci D 

PROVIDER: S-EPMC9190783 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature