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题名: Classification of Gesture based on sEMG Decomposition: A Preliminary Study
作者: 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: The 19th World Congress of the International Federation of Automatic Control
会议录出版者: IFAC
会议录出版地: Zürich, Switzerland
出版日期: 2014
页码: 2969-2974
收录类别: EI
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%.
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内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/15403
Appears in Collections:机器人学研究室_会议论文

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