A Review of Techniques for Muscle Fatigue Analysis and the Associated Noise Challenges
DOI:
https://doi.org/10.29304/jqcsm.2026.18.12484Keywords:
Electromyography (EMG) bio-signal,, muscle fatigue, noise, machine learning, deep learningAbstract
To reduce the risk of impression muscle fatigue in the medical field, sports, and rehabilitation of disorders and it is muscles are a critical neuromuscular phenomenon. Electromyography (EMG) is the most important Bio signal used to detect muscle fatigue. Many studies over the past few years have been conducted to address the challenge of muscle fatigue (detection, recognition, and prediction). This study presents a review of various approaches to build models, and evaluation metrics, and applications for each structure begins with exploring artificial intelligence (AI) methods such as machine learning (ML) and deep learning (DL), as well as hybrid model showing the way of data acquisition (sensors types, techniques, preprocessing, models…) specially noise affected of data collection of each type of power spectrum. Furthermore, this review compares fatigue detection, recognition, and prediction approaches, highlighting their performance, strengths, and limitations. Finally, a discussion of various aspects of bio-signal-based muscle fatigue, with specific applications and descriptions, and analyses of the datasets used in muscle fatigue to address the most trending issues and challenges in applying upper limb muscle fatigue. The synthesis presented here aims to guide future developments toward robust, interpretable, and real-time neuromuscular fatigue monitoring systems.
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