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MSFF-Net: Multi-Stream Feature Fusion Network for surface electromyography gesture recognition

In the field of surface electromyography (sEMG) gesture recognition, how to improve recognition accuracy has been a research hotspot. The rapid development of deep learning provides a new solution to this problem. At present, the main applications of deep learning for sEMG gesture feature extraction...

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Autores principales: Peng, Xiangdong, Zhou, Xiao, Zhu, Huaqiang, Ke, Zejun, Pan, Congcheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9639816/
https://www.ncbi.nlm.nih.gov/pubmed/36342906
http://dx.doi.org/10.1371/journal.pone.0276436
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author Peng, Xiangdong
Zhou, Xiao
Zhu, Huaqiang
Ke, Zejun
Pan, Congcheng
author_facet Peng, Xiangdong
Zhou, Xiao
Zhu, Huaqiang
Ke, Zejun
Pan, Congcheng
author_sort Peng, Xiangdong
collection PubMed
description In the field of surface electromyography (sEMG) gesture recognition, how to improve recognition accuracy has been a research hotspot. The rapid development of deep learning provides a new solution to this problem. At present, the main applications of deep learning for sEMG gesture feature extraction are based on convolutional neural network (CNN) structures to capture spatial morphological information of the multichannel sEMG or based on long short-term memory network (LSTM) to extract time-dependent information of the single-channel sEMG. However, there are few methods to comprehensively consider the distribution area of the sEMG signal acquisition electrode sensor and the arrangement of the sEMG signal morphological features and electrode spatial features. In this paper, a novel multi-stream feature fusion network (MSFF-Net) model is proposed for sEMG gesture recognition. The model adopts a divide-and-conquer strategy to learn the relationship between different muscle regions and specific gestures. Firstly, a multi-stream convolutional neural network (Multi-stream CNN) and a convolutional block attention module integrated with a resblock (ResCBAM) are used to extract multi-dimensional spatial features from signal morphology, electrode space, and feature map space. Then the learned multi-view depth features are fused by a view aggregation network consisting of an early fusion network and a late fusion network. The results of all subjects and gesture movement validation experiments in the sEMG signal acquired from 12 sensors provided by NinaPro’s DB2 and DB4 sub-databases show that the proposed model in this paper has better performance in terms of gesture recognition accuracy compared with the existing models.
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spelling pubmed-96398162022-11-08 MSFF-Net: Multi-Stream Feature Fusion Network for surface electromyography gesture recognition Peng, Xiangdong Zhou, Xiao Zhu, Huaqiang Ke, Zejun Pan, Congcheng PLoS One Research Article In the field of surface electromyography (sEMG) gesture recognition, how to improve recognition accuracy has been a research hotspot. The rapid development of deep learning provides a new solution to this problem. At present, the main applications of deep learning for sEMG gesture feature extraction are based on convolutional neural network (CNN) structures to capture spatial morphological information of the multichannel sEMG or based on long short-term memory network (LSTM) to extract time-dependent information of the single-channel sEMG. However, there are few methods to comprehensively consider the distribution area of the sEMG signal acquisition electrode sensor and the arrangement of the sEMG signal morphological features and electrode spatial features. In this paper, a novel multi-stream feature fusion network (MSFF-Net) model is proposed for sEMG gesture recognition. The model adopts a divide-and-conquer strategy to learn the relationship between different muscle regions and specific gestures. Firstly, a multi-stream convolutional neural network (Multi-stream CNN) and a convolutional block attention module integrated with a resblock (ResCBAM) are used to extract multi-dimensional spatial features from signal morphology, electrode space, and feature map space. Then the learned multi-view depth features are fused by a view aggregation network consisting of an early fusion network and a late fusion network. The results of all subjects and gesture movement validation experiments in the sEMG signal acquired from 12 sensors provided by NinaPro’s DB2 and DB4 sub-databases show that the proposed model in this paper has better performance in terms of gesture recognition accuracy compared with the existing models. Public Library of Science 2022-11-07 /pmc/articles/PMC9639816/ /pubmed/36342906 http://dx.doi.org/10.1371/journal.pone.0276436 Text en © 2022 Peng et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Peng, Xiangdong
Zhou, Xiao
Zhu, Huaqiang
Ke, Zejun
Pan, Congcheng
MSFF-Net: Multi-Stream Feature Fusion Network for surface electromyography gesture recognition
title MSFF-Net: Multi-Stream Feature Fusion Network for surface electromyography gesture recognition
title_full MSFF-Net: Multi-Stream Feature Fusion Network for surface electromyography gesture recognition
title_fullStr MSFF-Net: Multi-Stream Feature Fusion Network for surface electromyography gesture recognition
title_full_unstemmed MSFF-Net: Multi-Stream Feature Fusion Network for surface electromyography gesture recognition
title_short MSFF-Net: Multi-Stream Feature Fusion Network for surface electromyography gesture recognition
title_sort msff-net: multi-stream feature fusion network for surface electromyography gesture recognition
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9639816/
https://www.ncbi.nlm.nih.gov/pubmed/36342906
http://dx.doi.org/10.1371/journal.pone.0276436
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