Evaluation and Classification of Sprinting-Induced Muscle Fatigue Using Surface Electromyography and Machine Learning Techniques

doi:10.1016/j.irbm.2026.100936

Evaluation and Classification of Sprinting-Induced Muscle Fatigue Using Surface Electromyography and Machine Learning Techniques

2026-03-02

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Asjad Raza

R

Raghuram Karthik Desu *

DOI：10.1016/j.irbm.2026.100936

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• sEMG-based muscle fatigue analysis was carried out during sprinting. • Muscle fatigue levels were estimated from mean and median frequency shifts. • Thirteen fatigue features were derived using various time–frequency methods. • ML classifiers were evaluated for muscle fatigue detection using above features.

Keywords:

sEMG

muscle fatigue

sprinting

time–frequency analysis

machine learning

Evaluation and Classification of Sprinting-Induced Muscle Fatigue Using Surface Electromyography and Machine Learning Techniques

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