| 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
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| Department | 机器人学研究室 |
| Source Publication | IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
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| ISSN | 1534-4320 |
| 2019 | |
| Volume | 27Issue:5Pages:1071-1080 |
| Indexed By | SCI ; EI |
| EI Accession number | 20192006932485 |
| WOS ID | WOS:000467572900029 |
| Contribution Rank | 2 |
| Funding Organization | Fundamental Research Funds for the Central Universities ; National Natural Science Foundation of China |
| Keyword | Surface electromyography (sEMG) myoelectric prosthesis adaptive classifier online update |
| Abstract | 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. |
| Language | 英语 |
| WOS Subject | Engineering, Biomedical ; Rehabilitation |
| WOS Keyword | PROSTHESIS CONTROL ; EMG SIGNALS ; SURFACE EMG ; INFORMATION ; EXTRACTION ; SCHEME ; ROBUST ; SYSTEM |
| WOS Research Area | Engineering ; Rehabilitation |
| Funding Project | National Natural Science Foundation of China[U1813214] ; National Natural Science Foundation of China[61573340] ; National Natural Science Foundation of China[61503374] ; Fundamental Research Funds for the Central Universities[N182608004] ; 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] |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.sia.cn/handle/173321/24729 |
| Collection | 机器人学研究室 |
| Corresponding Author | Zhao XG(赵新刚) |
| Affiliation | 1.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. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Adaptive Hybrid Clas（2259KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text | |
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