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Classification of forearm EMG signals for 10 motions using optimum feature-channel combinations

Author

Listed:
  • Muhammad Shahzaib
  • Sadia Shakil
  • Sajid Ghuffar
  • Moazam Maqsood
  • Farrukh A. Bhatti

Abstract

Electromyography (EMG) is the study of electrical activity in the muscles. We classify EMG signals from surface electrodes (channels) using Artificial Neural Network (ANN). We evaluate classification performance of 10 different hand motions using several feature-channel combinations with wrapper method. Highest classification accuracy of 98.7% is achieved with each feature-channel combination. Compared to previous studies, we achieve the highest accuracy for 10 classes with lower number of feature-channel combination. We reduce ANN complexity without compromising the classification accuracy for deployment in low-end hardware with limited computational power along with improving the design of a low-cost hardware for EMG signal acquisition.

Suggested Citation

  • Muhammad Shahzaib & Sadia Shakil & Sajid Ghuffar & Moazam Maqsood & Farrukh A. Bhatti, 2021. "Classification of forearm EMG signals for 10 motions using optimum feature-channel combinations," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 24(9), pages 945-955, August.
    • Handle: RePEc:taf:gcmbxx:v:24:y:2021:i:9:p:945-955
    DOI: 10.1080/10255842.2020.1861256
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