Research on key technology of feature extraction based on surface EMG

Liye Ren; Chen Wang; Jianwei Fang; Junyi Tian; Jing Zhou

doi:10.1117/12.2636517

6 May 2022 Research on key technology of feature extraction based on surface EMG

Liye Ren, Chen Wang, Jianwei Fang, Junyi Tian, Jing Zhou

Proceedings Volume 12176, International Conference on Algorithms, Microchips and Network Applications; 1217618 (2022) https://doi.org/10.1117/12.2636517
Event: International Conference on Algorithms, Microchips, and Network Applications 2022, 2022, Zhuhai, China

Abstract

Surface Electromyographic(sEMG) is a kind of complex bioelectrical signal, and it is very important to select an appropriate feature extraction method. In this paper, the time domain analysis method, frequency domain analysis method and time-frequency domain analysis method are compared through experimental data, and the results of feature extraction of time-frequency domain analysis method are more representative, higher separation degree, and greatly reduce the one-sidedness of feature extraction. Finally, the energy eigenvalue of wavelet packet coefficient in timefrequency domain method is selected as the feature vector of signal pattern recognition to provide theoretical basis for real-time and accuracy of multi-motion pattern recognition.

Citation Download Citation

Liye Ren, Chen Wang, Jianwei Fang, Junyi Tian, and Jing Zhou "Research on key technology of feature extraction based on surface EMG", Proc. SPIE 12176, International Conference on Algorithms, Microchips and Network Applications, 1217618 (6 May 2022); https://doi.org/10.1117/12.2636517

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