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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
作者部门机器人学研究室
会议名称2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017
会议日期December 5-8, 2017
会议地点Macau, China
会议主办者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
会议录名称Proceedings of the 2017 IEEE International Conference on Robotics and Biomimetics
出版者IEEE
出版地New York
2017
页码1050-1055
收录类别EI
EI收录号20182905561247
产权排序1
ISBN号978-1-5386-3741-8
关键词—surface Electromyography (sEMG) incremental classifier online update motion recognition
摘要Traditional 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.
语种英语
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/22129
专题机器人学研究室
通讯作者Ding QC(丁其川)
作者单位1.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
推荐引用方式
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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