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

  • Shuang Gu
    ,
  • Yong Yue
    ,
  • Carsten Maple
    ,
  • Beisheng Liu
    ,
  • Chengdong Wu
Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Sustainable Development Goals

  • SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well

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.

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Publication milestones

  • Published - 13/09/2012

Publication status

Published - 13/09/2012

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

ISBN (Print)

9781467303125

ISBN (Electronic)

9781467303125

External Publication IDs

  • handle.net: 10547/270613
  • Scopus: 84868262853

Host publication title

nan

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