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Multi-scale EMG classification with spatial-temporal attention for prosthetic hands

Author

Listed:
  • Emimal M
  • W. Jino Hans
  • Inbamalar T.M
  • N. Mahiban Lindsay

Abstract

A classification framework for hand gestures using Electromyography (EMG) signals in prosthetic hands is presented. Leveraging the multi-scale characteristics and temporal nature of EMG signals, a Convolutional Neural Network (CNN) is used to extract multi-scale features and classify them with spatial-temporal attention. A multi-scale coarse-grained layer introduced into the input of one-dimensional CNN (1D-CNN) facilitates multi-scale feature extraction. The multi-scale features are fed into the attention layer and subsequently given to the fully connected layer to perform classification. The proposed model achieves classification accuracies of 93.4%, 92.8%, 91.3%, and 94.1% for Ninapro DB1, DB2, DB5, and DB7 respectively, thereby enhancing the confidence of prosthetic hand users.

Suggested Citation

  • Emimal M & W. Jino Hans & Inbamalar T.M & N. Mahiban Lindsay, 2025. "Multi-scale EMG classification with spatial-temporal attention for prosthetic hands," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 28(3), pages 337-352, February.
    • Handle: RePEc:taf:gcmbxx:v:28:y:2025:i:3:p:337-352
    DOI: 10.1080/10255842.2023.2287419
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