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

Content Provider	IEEE Xplore Digital Library
Author	Shuang Gu Yong Yue Maple, C. Beisheng Liu Chengdong Wu
Copyright Year	2012
Description	Author affiliation: School of Information Science and Engineering, Northeastern University, Shenyang, China (Chengdong Wu) \|\| Department of Computer Science and Technology, University of Bedfordshire, Luton, UK (Shuang Gu; Yong Yue; Maple, C.; Beisheng Liu)
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.
Starting Page	1158
Ending Page	1163
File Size	542821
Page Count	6
File Format	PDF
ISBN	9781467303125
e-ISBN	9781467303118
DOI	10.1109/INDIN.2012.6301140
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms Wrist Training assistive robot Service robots surface electromyography Neural networks wavelet Robot sensing systems neural network
Content Type	Text
Resource Type	Article

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