Ankle EMG signal feature extraction based on improved noise reduction method

Zhixian Mao; Hongsheng Wu; Changyuan Zhou

doi:10.1117/12.3034139

13 June 2024 Ankle EMG signal feature extraction based on improved noise reduction method

Zhixian Mao, Hongsheng Wu, Changyuan Zhou

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 1318030 (2024) https://doi.org/10.1117/12.3034139
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

Ankle joint is an important hub to control the stability of human walking. Its early rehabilitation training and accurate assessment of rehabilitation status are of great significance to the recovery of walking ability of hemiplegic patients. This paper proposes to evaluate the rehabilitation status of patients ' ankle joints based on surface electromyography signals. Firstly, the surface electromyography signals that affect the human ankle dorsiflexion tibialis anterior muscle are collected by the electromyography sensing device. Then, the empirical mode decomposition combined with the improved wavelet threshold denoising method is used to denoise, and a relatively pure electromyography signal is obtained. Finally, the amplitude, electromyography value and other eigenvalues of the surface electromyography signal are extracted to evaluate the rehabilitation status. The experimental results show that the quantitative evaluation of ankle rehabilitation status based on the de-noised surface electromyography signal provides a certain reference for the quantitative evaluation technology of rehabilitation.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhixian Mao, Hongsheng Wu, and Changyuan Zhou "Ankle EMG signal feature extraction based on improved noise reduction method", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 1318030 (13 June 2024); https://doi.org/10.1117/12.3034139

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