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

Emimal M, W. Jino Hans, Inbamalar T.m and N. Mahiban Lindsay

Computer Methods in Biomechanics and Biomedical Engineering, 2025, vol. 28, issue 3, 337-352

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.

Date: 2025
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DOI: 10.1080/10255842.2023.2287419

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