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Updated: Dec 25, 2025
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Ex Vivo Assessment of Contractility, Fatigability and Alternans in Isolated Skeletal Muscles
Published on: November 1, 2012
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Muscle fatigue analysis during dynamic contractions based on biomechanical features and Permutation Entropy
J Murillo-Escobar1, Y E Jaramillo-Munera1, D A Orrego-Metaute1
1Department of Exact and Applied Sciences, GI2B Research Group, Instituto Tecnologico Metropolitano ITM, CL 73 No. 76 A 354, Medellin, Colombia.
Mathematical Biosciences and Engineering : MBE
|April 3, 2020
Summary
This study introduces a new method for detecting muscle fatigue during dynamic movements using surface electromyography (sEMG) and biomechanical data. Permutation Entropy (PE) shows promise in identifying fatigue states more effectively than traditional sEMG features.
Area of Science:
- Sports Medicine
- Occupational Health
- Biomedical Engineering
Background:
- Muscle fatigue prediction is crucial in sports medicine and occupational health.
- Existing methods for muscle fatigue detection perform well in isometric contractions but less effectively in dynamic contractions.
- Surface electromyography (sEMG) and biomechanical signals offer potential for fatigue analysis.
Purpose of the Study:
- To develop and validate an approach for analyzing muscle fatigue during dynamic contractions.
- To investigate the effectiveness of Permutation Entropy (PE) and biomechanical features in detecting muscle fatigue.
- To compare the performance of PE with traditional sEMG features for muscle fatigue classification.
Main Methods:
- A protocol was established to induce fatigue in the deltoid muscle, acquiring sEMG and biomechanical signals.
- Biomechanical features (angle, angular velocity) were computed and correlated with fatigue progression to label contractions using hierarchical clustering.
- sEMG features, including PE, were analyzed for their discriminant capacity using ANOVA and ROC analysis.
Main Results:
- Biomechanical features derived from angle and angular velocity demonstrated a clear relationship with fatigue progression.
- Permutation Entropy (PE) effectively distinguished between Non-Fatigue, Transition-to-Fatigue, and Fatigue states.
- PE outperformed classical sEMG fatigue features, such as Median Frequency, in classifying fatigue levels.
Conclusions:
- Biomechanical data, specifically angle and angular velocity, are valuable indicators of muscle fatigue during dynamic contractions.
- Permutation Entropy (PE) offers a superior method for analyzing sEMG signals to detect and classify muscle fatigue states in dynamic movements.
- The proposed approach enhances the understanding and detection of muscle fatigue in dynamic scenarios, with implications for sports medicine and occupational health.
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