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

Authors

  • Xiaoyun Wang Anhui Vocational and Technical College

DOI:

https://doi.org/10.4108/eetpht.10.7230

Keywords:

Multi-stream Characteristics, Convolutional Neural Networks, Surface Electromyography Signal, Gestures, Recognition

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.

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Published

09-09-2024

How to Cite

1.
Wang X. Human Muscle sEMG Signal and Gesture Recognition Technology Based on Multi-Stream Feature Fusion Network. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Sep. 9 [cited 2024 Oct. 10];10. Available from: https://publications.eai.eu/index.php/phat/article/view/7230

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