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Muscle Recovery and Fatigue01:24

Muscle Recovery and Fatigue

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Muscle fatigue refers to the decline in a muscle's ability to maintain the force of contraction after prolonged activity. It primarily stems from changes within muscle fibers. Even before experiencing muscle fatigue, one may feel tired and have the urge to stop the activity. This response, known as central fatigue, occurs due to changes in the central nervous system, namely the brain and spinal cord. While there is no single mechanism that induces fatigue, it may serve as a protective...
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Muscle Fatigue in Dynamic Movement: Limitations and Challenges, Experimental Design, and New Research Horizons.

Natalia Daniel1, Jerzy Małachowski2, Kamil Sybilski2

  • 1Institute of Rocket Technology and Mechatronics, Faculty of Mechatronics, Armament and Aviation, Military University of Technology, 00-908 Warsaw, Poland.

Bioengineering (Basel, Switzerland)
|February 27, 2026
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Summary

Investigating muscle fatigue during dynamic movement presents challenges. Advanced methods like wavelet transform and AI show promise for accurate surface electromyography (sEMG) analysis in biomechanics.

Keywords:
AIDWTEMGdynamic movement

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Area of Science:

  • Biomechanics
  • Neuroscience
  • Sports Science

Background:

  • Muscle fatigue during dynamic movement is challenging to measure accurately using surface electromyography (sEMG).
  • Factors like skin impedance, electrode displacement, and subjective perception complicate sEMG analysis.
  • Classical metrics have limitations, necessitating advanced analytical approaches.

Purpose of the Study:

  • To outline guidelines and challenges for research on muscle fatigue in dynamic movement.
  • To focus on activity selection, equipment validation, sEMG signal analysis, and AI utilization.
  • To address the complexities of sEMG measurements in biomechanics.

Main Methods:

  • Utilizing time-frequency methods like wavelet transform (WT) for non-stationary sEMG signals.
  • Incorporating non-linear metrics (e.g., entropy) and multi-sensor data (EMG, accelerometers, fNIRS, EEG).
  • Applying artificial intelligence (AI) algorithms, particularly synergistic AI with WT for sEMG decomposition and extraction.

Main Results:

  • Wavelet transform (WT) and AI demonstrate efficacy in detecting muscle fatigue from sEMG signals.
  • Synergistic application of AI and WT improves the analysis of complex, dynamic sEMG data.
  • Multi-sensor data integration offers a more comprehensive understanding of fatigue.

Conclusions:

  • Further research is needed on system generalization and multi-sensor data integration for AI in fatigue detection.
  • Standardization of protocols and creation of public datasets are crucial for advancing dynamic muscle fatigue research.
  • AI combined with advanced signal processing techniques like WT holds significant potential for accurate muscle fatigue assessment.