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Gesture Classification in Electromyography Signals for Real-Time Prosthetic Hand Control Using a Convolutional Neural Network-Enhanced Channel Attention Model

Authors :
Guangjie Yu
Ziting Deng
Zhenchen Bao
Yue Zhang
Bingwei He
Source :
Bioengineering, Vol 10, Iss 11, p 1324 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Accurate and real-time gesture recognition is required for the autonomous operation of prosthetic hand devices. This study employs a convolutional neural network-enhanced channel attention (CNN-ECA) model to provide a unique approach for surface electromyography (sEMG) gesture recognition. The introduction of the ECA module improves the model’s capacity to extract features and focus on critical information in the sEMG data, thus simultaneously equipping the sEMG-controlled prosthetic hand systems with the characteristics of accurate gesture detection and real-time control. Furthermore, we suggest a preprocessing strategy for extracting envelope signals that incorporates Butterworth low-pass filtering and the fast Hilbert transform (FHT), which can successfully reduce noise interference and capture essential physiological information. Finally, the majority voting window technique is adopted to enhance the prediction results, further improving the accuracy and stability of the model. Overall, our multi-layered convolutional neural network model, in conjunction with envelope signal extraction and attention mechanisms, offers a promising and innovative approach for real-time control systems in prosthetic hands, allowing for precise fine motor actions.

Details

Language :
English
ISSN :
23065354
Volume :
10
Issue :
11
Database :
Directory of Open Access Journals
Journal :
Bioengineering
Publication Type :
Academic Journal
Accession number :
edsdoj.680cab4ec90a49a1ba2f6c14e7031ed9
Document Type :
article
Full Text :
https://doi.org/10.3390/bioengineering10111324