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

• Main Authors: AL‑Quraishi, Maged S., Ishak, Asnor J., Ahmad, Siti A., Hasan, Mohd K., Al‑Qurishi, Muhammad, Ghapanchizadeh, Hossein, Alamri, Atif
• Format: Article
• Language: English
• Published: Springer 2017
• 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; http://psasir.upm.edu.my/id/eprint/44064/; https://link.springer.com/article/10.1007%2Fs11517-016-1551-4
• Institution: Universiti Putra Malaysia

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.