Research on sports activity behavior prediction based on electromyography signal collection and intelligent sensing channel

Ye F. Zhao Y. Latif Z.
2025 PeerJ Inc.

PeerJ Computer Science
2025 #11

Sports behavior prediction requires precise and reliable analysis of muscle activity during exercise. This study proposes a multi-channel correlation feature extraction method for electromyographic (EMG) signals to overcome challenges in sports behavior prediction. A wavelet threshold denoising algorithm is enhanced with nonlinear function transitions and control coefficients to improve signal quality, achieving effective noise reduction and a higher signal-to-noise ratio. Furthermore, multi-channel linear and nonlinear correlation features are combined, leveraging mutual information estimation via copula entropy for feature construction. A stacking ensemble learning model, incorporating extreme gradient boosting (XGBoost), K-nearest network (KNN), Random Forest (RF), and naive Bayes (NB) as base learners, further enhances classification accuracy. Experimental results demonstrate that the proposed approach achieves over 95% prediction accuracy, significantly outperforming traditional methods. The robustness of multi-channel correlation features is validated across diverse datasets, proving their effectiveness in mitigating channel crosstalk and noise interference. This work provides a scientific basis for improving sports training strategies and reducing injury risks.

Copula entropy , Electromyographic signals , Multi channel correlation features , Mutual information , Wavelet thresholding

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College of Ocean Culture and Tourism, Xiamen Ocean Vocational College, Fujian, Xiamen, China
College of Information Engineering, Xiamen Ocean Vocational College, Xiamen, China
Department of Computer Science, School of Engineering and Digital Sciences (SEDS), Nazarbayev University, Astana, Kazakhstan

College of Ocean Culture and Tourism
College of Information Engineering
Department of Computer Science

10 лет помогаем публиковать статьи Международный издатель

Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026