Classification of ankle joint movements based on surface electromyography signals for rehabilitation robot applications
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 tec...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2017
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| Online Access: | http://psasir.upm.edu.my/id/eprint/44064/1/Classification%20of%20ankle%20joint%20movements%20based%20on%20surface%20electromyography%20signals%20for%20rehabilitation%20robot%20applications.pdf |
| _version_ | 1796974463068143616 |
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| author | AL‑Quraishi, Maged S. Ishak, Asnor J. Ahmad, Siti A. Hasan, Mohd K. Al‑Qurishi, Muhammad Ghapanchizadeh, Hossein Alamri, Atif |
| author_facet | AL‑Quraishi, Maged S. Ishak, Asnor J. Ahmad, Siti A. Hasan, Mohd K. Al‑Qurishi, Muhammad Ghapanchizadeh, Hossein Alamri, Atif |
| author_sort | AL‑Quraishi, Maged S. |
| collection | UPM |
| description | 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. |
| first_indexed | 2024-03-06T08:57:26Z |
| format | Article |
| id | upm.eprints-44064 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T08:57:26Z |
| publishDate | 2017 |
| publisher | Springer |
| record_format | dspace |
| spelling | upm.eprints-440642022-03-14T03:17:24Z http://psasir.upm.edu.my/id/eprint/44064/ Classification of ankle joint movements based on surface electromyography signals for rehabilitation robot applications AL‑Quraishi, Maged S. Ishak, Asnor J. Ahmad, Siti A. Hasan, Mohd K. Al‑Qurishi, Muhammad Ghapanchizadeh, Hossein Alamri, Atif 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. Springer 2017 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/44064/1/Classification%20of%20ankle%20joint%20movements%20based%20on%20surface%20electromyography%20signals%20for%20rehabilitation%20robot%20applications.pdf AL‑Quraishi, Maged S. and Ishak, Asnor J. and Ahmad, Siti A. and Hasan, Mohd K. and Al‑Qurishi, Muhammad and Ghapanchizadeh, Hossein and Alamri, Atif (2017) Classification of ankle joint movements based on surface electromyography signals for rehabilitation robot applications. Journal of Medical & Biology for Engineering and Computing, 55. pp. 747-758. ISSN 0140-0118; ESSN: 1741-0444 https://link.springer.com/article/10.1007%2Fs11517-016-1551-4 10.1007/s11517-016-1551-4 |
| spellingShingle | AL‑Quraishi, Maged S. Ishak, Asnor J. Ahmad, Siti A. Hasan, Mohd K. Al‑Qurishi, Muhammad Ghapanchizadeh, Hossein Alamri, Atif Classification of ankle joint movements based on surface electromyography signals for rehabilitation robot applications |
| title | Classification of ankle joint movements based on surface electromyography signals for rehabilitation robot applications |
| title_full | Classification of ankle joint movements based on surface electromyography signals for rehabilitation robot applications |
| title_fullStr | Classification of ankle joint movements based on surface electromyography signals for rehabilitation robot applications |
| title_full_unstemmed | Classification of ankle joint movements based on surface electromyography signals for rehabilitation robot applications |
| title_short | Classification of ankle joint movements based on surface electromyography signals for rehabilitation robot applications |
| title_sort | classification of ankle joint movements based on surface electromyography signals for rehabilitation robot applications |
| url | http://psasir.upm.edu.my/id/eprint/44064/1/Classification%20of%20ankle%20joint%20movements%20based%20on%20surface%20electromyography%20signals%20for%20rehabilitation%20robot%20applications.pdf |
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