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    Classification of multi-channels SEMG signals using wavelet and neural networks on assistive robot

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    Authors
    Gu, Shuang
    Yue, Yong
    Wu, Chengdong
    Maple, Carsten
    Liu, Beisheng
    Affiliation
    University of Bedfordshire, UK
    Issue Date
    2012
    Subjects
    assistive robot
    neural networks
    surface electromyography
    wavelet
    
    Metadata
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    Abstract
    Recently, the robot technology research is changing from manufacturing industry to non-manufacturing industry, especially the service industry related to the human life. Assistive robot is a kind of novel service robot. It can not only help the elder and disabled people to rehabilitate their impaired musculoskeletal functions, but also help healthy people to perform tasks requiring large forces. This kind of robot has a broad application prospect in many areas, such as medical rehabilitation, special military operations, special/high intensity physical labour, space, sports, and entertainment. SEMG (Surface Electromyography) of Palmaris longus, brachioradialis, flexor carpiulnaris and biceps brachii are analysed with a wavelet transform method. The absolute variance of 3-layer wavelet coefficients is distilled and regarded as signal characteristics to compose eigenvectors. The eigenvectors are input data of a neural network classifier used to identify 5 different kinds of movement patterns including wrist flexor, wrist extensor, elbow flexion, forearm pronation and forearm rotation. Experiments verify the effectiveness of the proposed method.
    Citation
    Shuang Gu; Yong Yue; Maple, C.; Beisheng Liu; Chengdong Wu; , "Classification of multi-channels SEMG signals using wavelet and neural networks on assistive robot," Industrial Informatics (INDIN), 2012 10th IEEE International Conference on , pp.1158-1163, 25-27 July 2012
    Publisher
    IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
    URI
    http://hdl.handle.net/10547/270613
    DOI
    10.1109/INDIN.2012.6301140
    Additional Links
    http://ieeexplore.ieee.org/xpls/abs_all.jsp?tp=&arnumber=6301140
    Type
    Conference papers, meetings and proceedings
    Language
    en
    ISBN
    9781467303125
    ae974a485f413a2113503eed53cd6c53
    10.1109/INDIN.2012.6301140
    Scopus Count
    Collections
    Centre for Research in Distributed Technologies (CREDIT)

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