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Adaptive Hybrid Classifier for Myoelectric Pattern Recognition Against the Interferences of Outlier Motion, Muscle Fatigue, and Electrode Doffing
Ding QC(丁其川)1; Zhao XG(赵新刚)2; Han JD(韩建达)3; Bu CG(卜春光)2; Wu CD(吴成东)1
Department机器人学研究室
Source PublicationIEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
ISSN1534-4320
2019
Volume27Issue:5Pages:1071-1080
Indexed BySCI ; EI
EI Accession number20192006932485
WOS IDWOS:000467572900029
Contribution Rank2
Funding OrganizationFundamental Research Funds for the Central Universities ; National Natural Science Foundation of China
KeywordSurface electromyography (sEMG) myoelectric prosthesis adaptive classifier online update
AbstractTraditional myoelectric prostheses that employ a static pattern recognition model to identify human movement intention from surface electromyography (sEMG) signals hardly adapt to the changes in the sEMG characteristics caused by interferences from daily activities, which hinders the clinical applications of such prostheses. In this paper, we focus on methods to reduce or eliminate the impacts of three types of daily interferences on myoelectric pattern recognition (MPR), i.e., outlier motion, muscle fatigue, and electrode doffing/donning. We constructed an adaptive incremental hybrid classifier (AIHC) by combining one-class support vector data description and multiclass linear discriminant analysis in conjunction with two specific update schemes. We developed an AIHC-based MPR strategy to improve the robustness of MPR against the three interferences. Extensive experiments on hand-motion recognition were conducted to demonstrate the performance of the proposed method. Experimental results show that the AIHC has significant advantages over non-adaptive classifiers under various interferences, with improvements in the classification accuracy ranging from 7.1% to 39% (p < 0.01). The additional evaluations on data deviations demonstrate that the AIHC can accommodate large-scale changes in the sEMG characteristics, revealing the potential of the AIHC-based MPR strategy in the development of clinical myoelectric prostheses.
Language英语
WOS SubjectEngineering, Biomedical ; Rehabilitation
WOS KeywordPROSTHESIS CONTROL ; EMG SIGNALS ; SURFACE EMG ; INFORMATION ; EXTRACTION ; SCHEME ; ROBUST ; SYSTEM
WOS Research AreaEngineering ; Rehabilitation
Funding ProjectFundamental Research Funds for the Central Universities[N182608004] ; National Natural Science Foundation of China[61503374] ; National Natural Science Foundation of China[61573340] ; National Natural Science Foundation of China[U1813214]
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/24729
Collection机器人学研究室
Corresponding AuthorZhao XG(赵新刚)
Affiliation1.Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110169, China
2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
3.College of Artificial Intelligence, Nankai University, Tianjin 300071, China
Recommended Citation
GB/T 7714
Ding QC,Zhao XG,Han JD,et al. Adaptive Hybrid Classifier for Myoelectric Pattern Recognition Against the Interferences of Outlier Motion, Muscle Fatigue, and Electrode Doffing[J]. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING,2019,27(5):1071-1080.
APA Ding QC,Zhao XG,Han JD,Bu CG,&Wu CD.(2019).Adaptive Hybrid Classifier for Myoelectric Pattern Recognition Against the Interferences of Outlier Motion, Muscle Fatigue, and Electrode Doffing.IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING,27(5),1071-1080.
MLA Ding QC,et al."Adaptive Hybrid Classifier for Myoelectric Pattern Recognition Against the Interferences of Outlier Motion, Muscle Fatigue, and Electrode Doffing".IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 27.5(2019):1071-1080.
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