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Classification of Gesture based on sEMG Decomposition: A Preliminary Study
Xiong AB(熊安斌); Zhang DH(张道辉); Zhao XG(赵新刚); Han JD(韩建达); Liu GJ(刘光军)
作者部门机器人学研究室
会议名称19th World Congress of the International Federation of Automatic Control
会议日期August 24-29, 2014
会议地点Cape Town, South Africa
会议录名称The 19th World Congress of the International Federation of Automatic Control
出版者IFAC
出版地Zürich, Switzerland
2014
页码2969-2974
收录类别EI ; CPCI(ISTP)
EI收录号20152200884889
WOS记录号WOS:000391107100480
产权排序1
关键词Semg Pattern Recognition Semg Decomposition Gaussian Mixture Model Linear Discriminate Analysis
摘要Multi-channel surface electromyography (sEMG) recognition has been investigated extensively by researchers over the past several decades. However, due to the nature of sEMG sensors, the more sensors are used, the greater chance for the sEMG to be influenced by environment noise. Furthermore, it is not feasible to use multi-sensors in some cases because of the bulky size of the sensors and the limited area of muscles. This paper proposes a novel sEMG recognition method based on the decomposition of single-channel sEMG. At first, sEMG is acquired while the participant does 5 predetermined hand gestures. Then, this signal is decomposed into its component motor unit potential trains (MUAPTs), which includes 4 steps: 2-order differential filtering, spikes detection, dimension reduction and clustering with Gaussian Mixture Model (GMM). Finally, 5 MUAPTs are obtained and used for hand gestures classification: four features, integral of absolute value (IAV), maximum value (MAX), median value of non-zero value (NonZeroMed) and index of NonZeroMed (Ind) are extracted to form feature matrix, which is then classified with the algorithm of Linear Discriminate Analysis (LDA). The classification results indicate this method can achieve an accuracy of 74.7% while the accuracy of traditional classification method for single-channel sEMG is about 52.6%.
语种英语
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文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/15403
专题机器人学研究室
通讯作者Xiong AB(熊安斌)
作者单位1.State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang, Liaoning, China
2.University of Chinese Academy of Sciences (CAS), Beijing, China
3.Department of Aerospace Engineering, Ryerson University, Toronto, Canada
推荐引用方式
GB/T 7714
Xiong AB,Zhang DH,Zhao XG,et al. Classification of Gesture based on sEMG Decomposition: A Preliminary Study[C]. Zürich, Switzerland:IFAC,2014:2969-2974.
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