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Updated: Sep 9, 2025

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Study on Muscle Fatigue Classification for Manual Lifting by Fusing sEMG and MMG Signals.

Zheng Wang1, Xiaorong Guan1,2, Dingzhe Li1

  • 1School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.

Sensors (Basel, Switzerland)
|August 28, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for analyzing muscle fatigue during heavy lifting by fusing surface electromyography (sEMG) and mechanomyography (MMG) signals. The back-propagation neural network and bidirectional encoder representation from the transformer (BP + BERT) algorithm achieved 98.10% accuracy in classifying muscle fatigue.

Keywords:
back-propagation neural network and BERTdeep learningmechanomyographymuscle fatiguesurface electromyography

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Area of Science:

  • Biomedical Engineering
  • Occupational Health
  • Machine Learning Applications

Background:

  • Manual lifting of heavy loads poses risks of muscle fatigue and potential irreversible impairment.
  • Accurate analysis of muscle fatigue is crucial for preventing occupational injuries.
  • Existing methods may not fully capture the complexities of muscle fatigue during dynamic tasks.

Purpose of the Study:

  • To propose and evaluate a novel signal fusion method for analyzing muscle fatigue during manual lifting.
  • To investigate the inaugural application of the back-propagation neural network and bidirectional encoder representation from the transformer (BP + BERT) algorithm for fused sensor data in muscle fatigue analysis.
  • To compare the performance of different machine learning algorithms in classifying muscle fatigue using combined sEMG and MMG signals.

Main Methods:

  • Conducted manual lifting fatigue tests on 16 participants, collecting surface electromyography (sEMG) and mechanomyography (MMG) signals.
  • Extracted mean power frequency (MPF) eigenvalues from both sEMG and MMG signals to label muscle fatigue.
  • Fused sEMG and MMG data into three datasets and classified muscle fatigue using SVM+RBF, SVM+BERT, BP, and BP+BERT algorithms.

Main Results:

  • The fusion of sEMG and MMG signals demonstrated effectiveness in analyzing muscle fatigue.
  • The back-propagation neural network and bidirectional encoder representation from the transformer (BP + BERT) algorithm achieved the highest average accuracy of 98.10% in muscle fatigue classification.
  • The BP + BERT algorithm showed enhanced performance when utilizing the fused sEMG and MMG dataset.

Conclusions:

  • Signal fusion of sEMG and MMG is a viable and effective strategy for muscle fatigue analysis during manual lifting.
  • The BP + BERT algorithm offers a powerful and accurate approach for muscle fatigue classification, outperforming other tested methods.
  • This study highlights the potential of advanced machine learning techniques combined with multi-sensor data fusion for improving occupational safety and understanding muscle fatigue.