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Classification of human hand movements using surface EMG for myoelectric control

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conference contribution
posted on 2017-03-24, 09:38 authored by Jiefei Wei, Qinggang MengQinggang Meng, Atta Badii
© Springer International Publishing AG 2017.Surface electromyogram (sEMG) is a bioelectric signal that can be captured non-invasively by placing electrodes on the human skin. The sEMG is capable of representing the action intent of nearby muscles. The research of myoelectric control using sEMG has been primarily driven by the potential to create humanmachine interfaces which respond to users intentions intuitively. However, it is one of the major gaps between research and commercial applications that there are rarely robust simultaneous control schemes. This paper proposes one classification method and a potential real-time control scheme. Four machine learning classifiers have been tested and compared to find the best configuration for different potential applications, and non-negative matrix factorisation has been used as a pre-processing tool for performance improvement. This control scheme achieves its highest accuracy when it is adapted to a single user at a time. It can identify intact subjects hand movements with above 98% precision and 91% upwards for amputees but takes double the amount of time for decision-making.

History

School

  • Science

Department

  • Computer Science

Published in

the 16th UK Workshop on Computational Intelligence Advances in Intelligent Systems and Computing

Pages

339 - 339

Citation

WEI, J. MENG, Q. and BADII, A., 2017. Classification of human hand movements using surface EMG for myoelectric control. IN: Plamen, A. ...et al. (eds.), Advances in Computational Intelligence Systems: Contributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK, Cham, Switzerland: Springer, pp. 331-339.

Publisher

Springer

Version

  • AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2017

Notes

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-46562-3_22.

ISBN

9783319465616

ISSN

2194-5357

Language

  • en

Location

Lancaster, UK

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