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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)
Department机器人学研究室
Conference Name19th World Congress of the International Federation of Automatic Control
Conference DateAugust 24-29, 2014
Conference PlaceCape Town, South Africa
Source PublicationThe 19th World Congress of the International Federation of Automatic Control
PublisherIFAC
Publication PlaceZürich, Switzerland
2014
Pages2969-2974
Indexed ByEI
EI Accession number20152200884889
Contribution Rank1
KeywordSemg Pattern Recognition Semg Decomposition Gaussian Mixture Model Linear Discriminate Analysis
AbstractMulti-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%.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/15403
Collection机器人学研究室
Corresponding AuthorXiong AB(熊安斌)
Affiliation1.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
Recommended Citation
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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