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  3. Muscle Fatigue Assessment In Healthcare Application By Using Surface Electromyography: A Transfer Learning Approach.
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Muscle Fatigue Assessment in Healthcare Application by Using Surface Electromyography: A Transfer Learning Approach.

Andrea Manni1, Gabriele Rescio1, Andrea Caroppo1

  • 1Institute for Microelectronics and Microsystems, National Research Council of Italy, 73100 Lecce, Italy.

Sensors (Basel, Switzerland)
|January 28, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study developed a deep learning framework to monitor muscle fatigue in elderly individuals using electromyography (EMG) signals. The novel approach accurately classifies fatigue levels, enhancing safety for assisted living applications.

Keywords:
muscle fatiguesurface electromyographytransfer learning

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence
  • Gerontology

Background:

  • Muscle fatigue monitoring is crucial for elderly safety and activity support.
  • Existing methods for fatigue assessment have limitations in real-time application.
  • Ambient Assisted Living (AAL) requires non-invasive, reliable monitoring solutions.

Purpose of the Study:

  • To introduce a novel deep learning framework for classifying muscle fatigue levels.
  • To utilize wireless surface electromyographic (sEMG) data for fatigue detection.
  • To support the development of AAL applications for elderly care.

Main Methods:

  • A new dataset of sEMG signals was collected from elderly and non-elderly adults.
  • One-dimensional sEMG signals were transformed into two-dimensional time-frequency images (scalograms) using Continuous Wavelet Transform.
  • Pre-trained Convolutional Neural Networks (CNNs) were fine-tuned for image classification, including binary and multiclass fatigue level detection.

    • Main Results:

    • The deep learning framework achieved 98.6% accuracy in binary classification (fatigued vs. non-fatigued).
    • A multiclass classification achieved 95.6% accuracy (No Fatigue, Moderate Fatigue, Hard Fatigue).
    • The proposed transfer learning pipeline outperformed traditional Machine Learning methods (max 92% accuracy).

    Conclusions:

    • The proposed deep learning framework demonstrates robust and generalizable performance for muscle fatigue monitoring.
    • This approach offers a potential real-time, non-invasive solution for AAL scenarios.
    • The findings support the integration of advanced AI for enhanced elderly care and safety.