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Article type: Research Article
Authors: Hou, Yuntonga | Shang, Shuyeb; * | Cao, Shengxic | Liu, Zhengjiad
Affiliations: [a] Department of Physical Science and Technology, Wuhan University, Wuhan, China | [b] Department of Electrical Engineering and Automation, Wuhan University, Wuhan, China | [c] Chengdu Jiaxiang Foreign Language School, Chengdu, China | [d] College of Engineering, University of Wisconsin-Madison, Madison, United States
Correspondence: [*] Corresponding author. Shuye Shang, Department of Electrical Engineering and Automation, Wuhan University, Wuhan, 430072, China. E-mail:[email protected].
Abstract: A robust muscle fatigue algorithm plays a pivotal role in depicting the degree of muscle fatigue in both time-series EMG signal graphs and spectral graphs, aligning with human perception. While the fuzzy approximate entropy (fApEn) algorithm has been enhanced from the foundation of approximate entropy (ApEn) through the incorporation of fuzzy affiliation, concerns persist regarding the threshold value and the algorithm’s application range. This study extracts EMG signals across varied time durations and head-down angles, employing enhanced signal preprocessing techniques and optimizing the fApEn algorithm. Furthermore, real-time fatigue perceptions of subjects were recorded using the rating of perceived exertion. Experimental outcomes reveal that the EMG signal, post-wavelet analysis preprocessing, demonstrates promising noise reduction capabilities. Notably, the fApEn algorithm exhibits considerable enhancements through the identification of an optimal threshold using the gradient descent algorithm and a machine learning strategy.
Keywords: EMG, muscle fatigue, fuzzy approximate entropy, wavelet transform, machine learning
DOI: 10.3233/JIFS-237293
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8049-8063, 2024
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