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

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...

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Bibliographic Details
Published in:Frontiers in neurorobotics Vol. 16; p. 862193
Main Authors: Wu, Le, Chen, Xun, Chen, Xiang, Zhang, Xu
Format: Journal Article
Language:English
Published: Switzerland Frontiers Research Foundation 28.03.2022
Frontiers Media S.A
Subjects:
Classification
Discriminant analysis
Electrodes
Electromyography
Experiments
Forearm
human-machine interactions
Information processing
Neural networks
Neuroscience
novelty detection
Pattern recognition
Prostheses
human-machine interactions
novelty detection
electromyography
neural networks
pattern recognition
ISSN:1662-5218, 1662-5218
Online Access:Get full text
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Summary: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 different from those from target patterns. Therefore, it was straightforward to discriminate and then reject the novel motion interferences identified as untargeted and unlearned patterns. The performance of the proposed method was evaluated with HD-sEMG data recorded by two 8 × 6 electrode arrays placed over the forearm extensors and flexors of 9 subjects performing seven target motion tasks and six novel motion tasks. The proposed method achieved high accuracies over 95% for identifying and rejecting novel motion tasks, and it outperformed conventional methods with statistical significance ( p < 0.05). The proposed method is demonstrated to be a promising solution for rejecting novel motion interferences, which are ubiquitous in myoelectric control. This study will enhance the robustness of the myoelectric control system against novelty interference.
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Edited by: Hang Su, Fondazione Politecnico di Milano, Italy
Reviewed by: Jing Luo, Wuhan Institute of Technology, China; Xinxing Chen, Southern University of Science and Technology, China
ISSN:1662-5218
1662-5218
DOI:10.3389/fnbot.2022.862193