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An incremental EMG classification model to detect and recognize randomly-occurred outlier motion
Ding QC(丁其川)1; Zhao XG(赵新刚)1; Li ZY(李自由)1,2; Han JD(韩建达)1
Department机器人学研究室
Conference Name2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017
Conference DateDecember 5-8, 2017
Conference PlaceMacau, China
Author of SourceBeijing Institute of Technology ; City University of Hong Kong ; IEEE Robotics and Automation Society ; Shenzhen Academy of Robotics ; University of Hong Kong ; University of Macau
Source PublicationProceedings of the 2017 IEEE International Conference on Robotics and Biomimetics
PublisherIEEE
Publication PlaceNew York
2017
Pages1050-1055
Indexed ByEI
EI Accession number20182905561247
Contribution Rank1
ISBN978-1-5386-3741-8
Keyword—surface Electromyography (sEMG) incremental classifier online update motion recognition
AbstractTraditional EMG-motion recognition methods are only able to recognize target motions that presented in the training phase, but cannot detect randomly-occurred outlier motions that did not present before. Here, a hybrid classifier that combines one-class SVMs and a multi-class LDA was proposed to perform recognition on target classes and rejection on outlier classes. The classification ability of the hybrid classifier can incrementally grow via online learning the data of outlier classes. Extensive experiments on EMG-based hand-motion recognition were conducted to verify the performance of the incremental hybrid classifier (IHC). The mean recognition accuracy on target classes of IHC is 92%, which is 23% higher than that of the normal MLP. Moreover, IHC has the ability to detect outlier patterns that MLP would misclassify to target classes.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/22129
Collection机器人学研究室
Corresponding AuthorDing QC(丁其川)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
Recommended Citation
GB/T 7714
Ding QC,Zhao XG,Li ZY,et al. An incremental EMG classification model to detect and recognize randomly-occurred outlier motion[C]//Beijing Institute of Technology, City University of Hong Kong, IEEE Robotics and Automation Society, Shenzhen Academy of Robotics, University of Hong Kong, University of Macau. New York:IEEE,2017:1050-1055.
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