Adaptive Hybrid Classifier for Myoelectric Pattern Recognition Against the Interferences of Outlier Motion, Muscle Fatigue, and Electrode Doffing

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 机器人学研究室

	Adaptive Hybrid Classifier for Myoelectric Pattern Recognition Against the Interferences of Outlier Motion, Muscle Fatigue, and Electrode Doffing
	Han JD(韩建达)1 ; Wu CD(吴成东)3; Bu CG(卜春光)2 ; Zhao XG(赵新刚)2 ; Ding QC(丁其川)3
刊名	IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
	2019
卷号	27 期号:5 页码:1071-1080
关键词	Surface electromyography (sEMG) myoelectric prosthesis adaptive classifier online update
ISSN号	1534-4320
产权排序	2
英文摘要	Traditional 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.
资助项目	Fundamental 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]
WOS关键词	PROSTHESIS CONTROL ; EMG SIGNALS ; SURFACE EMG ; INFORMATION ; EXTRACTION ; SCHEME ; ROBUST ; SYSTEM
WOS研究方向	Engineering ; Rehabilitation
语种	英语
WOS记录号	WOS:000467572900029
资助机构	Fundamental Research Funds for the Central Universities ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.sia.cn/handle/173321/24729]
专题	沈阳自动化研究所_机器人学研究室
通讯作者	Zhao XG(赵新刚)
作者单位	1.College of Artificial Intelligence, Nankai University, Tianjin 300071, China 2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 3.Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110169, China
推荐引用方式 GB/T 7714	Han JD,Wu CD,Bu CG,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	Han JD,Wu CD,Bu CG,Zhao XG,&Ding QC.(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	Han JD,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.