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phat 24(1):

Editorial

Human Muscle sEMG Signal and Gesture Recognition Technology Based on Multi-Stream Feature Fusion Network

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  • @ARTICLE{10.4108/eetpht.10.7230,
        author={Xiaoyun Wang},
        title={Human Muscle sEMG Signal and Gesture Recognition Technology Based on Multi-Stream Feature Fusion Network},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={10},
        number={1},
        publisher={EAI},
        journal_a={PHAT},
        year={2024},
        month={9},
        keywords={Multi-stream Characteristics, Convolutional Neural Networks, Surface Electromyography Signal, Gestures, Recognition},
        doi={10.4108/eetpht.10.7230}
    }
    
  • Xiaoyun Wang
    Year: 2024
    Human Muscle sEMG Signal and Gesture Recognition Technology Based on Multi-Stream Feature Fusion Network
    PHAT
    EAI
    DOI: 10.4108/eetpht.10.7230
Xiaoyun Wang1,*
  • 1: Anhui Vocational and Technical College
*Contact email: wangxy@uta.edu.cn

Abstract

Surface electromyography signals have significant value in gesture recognition due to their ability to reflect muscle activity in real time. However, existing gesture recognition technologies have not fully utilized surface electromyography signals, resulting in unsatisfactory recognition results. To this end, firstly, a Butterworth filter was adopted to remove high-frequency noise from the signal. A combined method of moving translation threshold was introduced to extract effective signals. Then, a gesture recognition model based on multi-stream feature fusion network was constructed. Feature extraction and fusion were carried out through multiple parallel feature extraction paths, combined with convolutional neural networks and residual attention mechanisms. Compared to popular methods of the same type, this new recognition method had the highest recognition accuracy of 92.1% and the lowest recognition error of 5%. Its recognition time for a single-gesture image was as short as 4s, with a maximum Kappa coefficient of 0.92. Therefore, this method combining multi-stream feature fusion networks can effectively improve the recognition accuracy and robustness of gestures and has high practical value.

Keywords
Multi-stream Characteristics, Convolutional Neural Networks, Surface Electromyography Signal, Gestures, Recognition
Received
2024-06-24
Accepted
2024-09-02
Published
2024-09-09
Publisher
EAI
http://dx.doi.org/10.4108/eetpht.10.7230

Copyright © 2024 Xiaoyun Wang, licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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