Rejecting Novel Motions in High-Density Myoelectric Pattern Recognition Using Hybrid Neural Networks.

ABSTRACT: The objective of this study is to develop a method for alleviating a novel pattern interference toward achieving a robust myoelectric pattern-recognition control system. To this end, a framework was presented for surface electromyogram (sEMG) pattern classification and novelty detection using hybrid neural networks, i.e., a convolutional neural network (CNN) and autoencoder networks. In the framework, the CNN was first used to extract spatio-temporal information conveyed in the sEMG data recorded via high-density (HD) 2-dimensional electrode arrays. Given the target motion patterns well-characterized by the CNN, autoencoder networks were applied to learn variable correlation in the spatio-temporal information, where samples from any novel pattern appeared to be significantly differe

SUBMITTER: Wu L 

PROVIDER: S-EPMC8996371 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature