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ORIGINAL RESEARCH article
Front. Bioeng. Biotechnol.
Sec. Biosensors and Biomolecular Electronics
Volume 13 - 2025 | doi: 10.3389/fbioe.2025.1487020
sEMG-based Gesture Recognition using Multi-Stream Adaptive CNNs with Integrated Residual Modules
Provisionally accepted- Shandong University of Traditional Chinese Medicine, Jinan, China
Convolutional neural networks are widely used in gesture recognition research, which employs surface electromyography. However, when processing surface electromyography data, current deep learning models still face challenges, such as insufficient effective feature extraction, poor performance in multi-gesture recognition, and low accuracy in recognizing sparse surface electromyography. To address these issues, this study proposed a multi-stream adaptive convolutional neural networks with residual modules (MSACNN-RM) for surface electromyography gesture recognition, which integrates multiple streams of convolutional neural networks , adaptive convolutional neural networks , and residual modules to enhance the model's feature extraction and learning capabilities. This improves the model's ability to extract and understand complex data patterns. The experimental results demonstrated that the model achieved recognition accuracies of 98.24%, 93.52%, and 92.27% respectively on the Ninapro DB1, Ninapro DB2, and Ninapro DB4 datasets. Compared with other deep learning models, MSACNN-RM achieves higher accuracy compared to existing models.
Keywords: sEMG, gesture recognition, Multi-Stream Convolutional Neural Network, Residual Modules, Adaptive Convolutional Neural Networks
Received: 27 Aug 2024; Accepted: 06 Mar 2025.
Copyright: © 2025 XIA, Qiu, Zhang and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Dawei Qiu, Shandong University of Traditional Chinese Medicine, Jinan, China
Jing Liu, Shandong University of Traditional Chinese Medicine, Jinan, China
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