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Updated: Aug 11, 2025

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A New Approach to Noninvasive-Prolonged Fatigue Identification Based on Surface EMG Time-Frequency and Wavelet

Fauzani N Jamaluddin1, Fatimah Ibrahim1,2,3, Siti A Ahmad4,5

  • 1Center for Innovation in Medical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia.

Journal of Healthcare Engineering
|February 9, 2023
PubMed
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This study identifies prolonged fatigue (PF) in athletes using surface electromyography (EMG) signals. Surface EMG effectively detects early signs of PF, aiding in injury prevention and performance optimization.

Area of Science:

  • Sports Science
  • Biomedical Engineering
  • Physiology

Background:

  • Fatigue management is crucial in sports for performance and injury prevention.
  • Overtraining, or prolonged fatigue (PF), can negatively impact athletes.
  • Surface electromyography (EMG) offers a potential method for monitoring muscle fatigue.

Purpose of the Study:

  • To identify and classify prolonged fatigue (PF) using surface EMG signals.
  • To investigate the changes in EMG signals during the onset of PF.
  • To evaluate the efficacy of different EMG features and classifiers for PF detection.

Main Methods:

  • An experiment involving twenty participants was conducted.
  • Prolonged fatigue (PF) was induced using a five-day Bruce Protocol treadmill test.

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  • Surface EMG signals were recorded from four lower extremity muscles: biceps femoris (BF), rectus femoris (RF), vastus medialis (VM), and vastus lateralis (VL).
  • Time, frequency, and wavelet index features of EMG signals were analyzed.
  • A naïve Bayes (NB) classifier was employed for PF identification.

    • Main Results:

    • The experimental protocol successfully induced symptoms of PF, including soreness, lethargy, and performance decrement.
    • Changes in frequency features (ΔFmed and ΔFmean) and time features (ΔRMS and ΔMAV) of surface EMG indicated the progression of PF.
    • Wavelet index features demonstrated utility in PF identification.
    • The naïve Bayes classifier achieved high accuracy in distinguishing PF: 98% for RF, 94% for BF, 97% for VM, and 9% for VL.

    Conclusions:

    • Surface EMG signals can effectively identify the onset of prolonged fatigue (PF) in athletes.
    • Analysis of time and frequency domain features of EMG signals provides valuable insights into PF progression.
    • This research has significant implications for preventing overtraining and optimizing athlete recovery in sports.