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Machine learning-based sEMG Signal Classification for Hand Gesture Recognition
Aarotale, Parshuram N. Rattani, Ajita
Aarotale, Parshuram N.
Rattani, Ajita
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Issue Date
2024-12-06
Type
Conference paper
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Keywords
Deep Learning Models,Electromyographic (EMG) signals,Machine learning
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Citation
P. N. Aarotale and A. Rattani, "Machine Learning-based sEMG Signal Classification for Hand Gesture Recognition," 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Lisbon, Portugal, 2024, pp. 6319-6326, doi: 10.1109/BIBM62325.2024.10822133.
Abstract
EMG-based hand gesture recognition uses electromyographic (EMG) signals to interpret and classify hand movements by analyzing electrical activity generated by muscle contractions. It has wide applications in prosthesis control, rehabilitation training, and human-computer interaction. Using electrodes placed on the skin, the EMG sensor captures muscle signals, which are processed and filtered to reduce noise. Numerous feature extraction and machine learning algorithms have been proposed to extract and classify muscle signals to distinguish between various hand gestures.This paper aims to benchmark the performance of EMG-based hand gesture recognition using novel feature extraction methods, namely, fused time-domain descriptors, temporal-spatial descriptors, and wavelet transform-based features, combined with the state-of-the-art machine and deep learning models. Experimental investigations on the Grabmyo dataset demonstrate that the 1D Dilated CNN performed the best with an accuracy of 97% using fused time-domain descriptors such as power spectral moments, sparsity, irregularity factor and waveform length ratio. Similarly, on the FORS-EMG dataset, random forest performed the best with an accuracy of 94.95% using temporal-spatial descriptors (which include time domain features along with additional features such as coefficient of variation (COV), and Teager-Kaiser energy operator (TKEO)). © 2024 IEEE.
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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Publisher
Institute of Electrical and Electronics Engineers Inc.
Journal
Book Title
Series
2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
3 December 2024 through 6 December 2024
Lisbon
205987
3 December 2024 through 6 December 2024
Lisbon
205987