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Classification of ankle joint movements based on surface electromyography signals for rehabilitation robot applications

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Abstract

Electromyography (EMG)-based control is the core of prostheses, orthoses, and other rehabilitation devices in recent research. Nonetheless, EMG is difficult to use as a control signal given the complex nature of the signal. To overcome this problem, the researchers employed a pattern recognition technique. EMG pattern recognition mainly involves four stages: signal detection, preprocessing feature extraction, dimensionality reduction, and classification. In particular, the success of any pattern recognition technique depends on the feature extraction stage. In this study, a modified time-domain features set and logarithmic transferred time-domain features (LTD) were evaluated and compared with other traditional time-domain features set (TTD). Three classifiers were employed to assess the two feature sets, namely linear discriminant analysis (LDA), k nearest neighborhood, and Naïve Bayes. Results indicated the superiority of the new time-domain feature set LTD, on conventional time-domain features TTD with the average classification accuracy of 97.23 %. In addition, the LDA classifier outperformed the other two classifiers considered in this study.

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Acknowledgments

This work was financialy supported by the King Saud University through the Vice Deanship of Research Chairs,Chair of Pervasive and Mobile Computing and Ministry of Higher Education of Malaysia through the FRGS grant.

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Universiti Putra Malaysia, 43400, Serdang, Malaysia

    Maged S. AL-Quraishi, Asnor J. Ishak, Siti A. Ahmad, Mohd K. Hasan & Hossein Ghapanchizadeh

  2. Research Chair of Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, Riydh, 11543, Saudi Arabia

    Muhammad Al-Qurishi & Atif Alamri

Authors
  1. Maged S. AL-Quraishi
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  2. Asnor J. Ishak
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  3. Siti A. Ahmad
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  4. Mohd K. Hasan
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  5. Muhammad Al-Qurishi
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  6. Hossein Ghapanchizadeh
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Correspondence to Maged S. AL-Quraishi.

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AL-Quraishi, M.S., Ishak, A.J., Ahmad, S.A. et al. Classification of ankle joint movements based on surface electromyography signals for rehabilitation robot applications. Med Biol Eng Comput 55, 747–758 (2017). https://doi.org/10.1007/s11517-016-1551-4

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  • DOI: https://doi.org/10.1007/s11517-016-1551-4

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