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Deep Learning for EMG-based Human-Machine Interaction: A Review
Xiong DZ(熊德臻)1,2,3; Zhang DH(张道辉)1,2; Zhao XG(赵新刚)1,2; Zhao YW(赵忆文)1,2
Department机器人学研究室
Source PublicationIEEE/CAA Journal of Automatica Sinica
ISSN2329-9266
2021
Volume8Issue:3Pages:512-533
Indexed BySCI ; EI
EI Accession number20210609903723
WOS IDWOS:000615043100002
Contribution Rank1
Funding OrganizationNational Natural Science Foundation of China (U1813214, 61773369, 61903360) ; Selfplanned Project of the State Key Laboratory of Robotics (2020-Z12) ; China Postdoctoral Science Foundation funded project (2019M661155)
KeywordAccuracy deep learning electromyography (EMG) human-machine interaction (HMI) robustness
Abstract

Electromyography (EMG) has already been broadly used in human-machine interaction (HMI) applications. Determining how to decode the information inside EMG signals robustly and accurately is a key problem for which we urgently need a solution. Recently, many EMG pattern recognition tasks have been addressed using deep learning methods. In this paper, we analyze recent papers and present a literature review describing the role that deep learning plays in EMG-based HMI. An overview of typical network structures and processing schemes will be provided. Recent progress in typical tasks such as movement classification, joint angle prediction, and force/torque estimation will be introduced. New issues, including multimodal sensing, inter-subject/inter-session, and robustness toward disturbances will be discussed. We attempt to provide a comprehensive analysis of current research by discussing the advantages, challenges, and opportunities brought by deep learning. We hope that deep learning can aid in eliminating factors that hinder the development of EMG-based HMI systems. Furthermore, possible future directions will be presented to pave the way for future research.

Language英语
WOS SubjectAutomation & Control Systems
WOS Research AreaAutomation & Control Systems
Funding ProjectNational Natural Science Foundation of China[U1813214] ; National Natural Science Foundation of China[61773369] ; National Natural Science Foundation of China[61903360] ; Self-planned Project of the State Key Laboratory of Robotics[2020-Z12] ; China Postdoctoral Science Foundation[2019M661155]
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/28342
Collection机器人学研究室
Corresponding AuthorZhang DH(张道辉); Zhao XG(赵新刚)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
3.University of Chinese Academy of Sciences, Beijing 100049, China
Recommended Citation
GB/T 7714
Xiong DZ,Zhang DH,Zhao XG,et al. Deep Learning for EMG-based Human-Machine Interaction: A Review[J]. IEEE/CAA Journal of Automatica Sinica,2021,8(3):512-533.
APA Xiong DZ,Zhang DH,Zhao XG,&Zhao YW.(2021).Deep Learning for EMG-based Human-Machine Interaction: A Review.IEEE/CAA Journal of Automatica Sinica,8(3),512-533.
MLA Xiong DZ,et al."Deep Learning for EMG-based Human-Machine Interaction: A Review".IEEE/CAA Journal of Automatica Sinica 8.3(2021):512-533.
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