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A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers
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Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors.

Sophia Otálora1, Marcelo E V Segatto1, Maxwell E Monteiro2

  • 1Telecommunications Laboratory (LabTel), Electrical Engineering Department, Federal University of Espírito Santo (UFES), Vitória 290075-910, Brazil.

Sensors (Basel, Switzerland)
|November 25, 2023
PubMed
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This summary is machine-generated.

This study introduces a wearable sensor model to detect muscle fatigue during repetitive lifting tasks. The model accurately estimates fatigue levels, aiding in preventing work-related musculoskeletal disorders.

Area of Science:

  • Biomechanics
  • Wearable Technology
  • Machine Learning

Background:

  • Muscle fatigue hinders worker performance and well-being, particularly in repetitive tasks.
  • Traditional electromyography (EMG) has limitations for long-term monitoring in occupational settings.
  • Wearable, non-invasive devices offer a practical alternative for assessing muscle fatigue.

Purpose of the Study:

  • To develop and validate a computational model for estimating muscle fatigue using wearable sensors.
  • To compare the efficacy of different sensor combinations (Optical Fiber Sensors - OFS, Inertial Measurement Units - IMU, EMG) for fatigue detection.
  • To identify key biomechanical features indicative of muscle fatigue.

Main Methods:

  • 30 subjects performed repetitive arm lifting tasks until fatigue.
Keywords:
Optical Fiber Sensorselectromyographyinertial sensorsmachine learningmuscle fatigue

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  • Data collected included muscle activity (EMG), elbow angles, velocities, and subjective Borg scale ratings.
  • Machine learning algorithms, including LightGBM, were employed to classify fatigue states (low, moderate, high).

    • Main Results:

    • The LightGBM model achieved 96.2% accuracy using all sensors and 33 features.
    • A model using only OFS and IMU sensors (13 features) reached 95.4% accuracy.
    • Key features for fatigue estimation included elbow angles, wrist velocities, acceleration variations, and neck movements.

    Conclusions:

    • A computational model utilizing OFS and IMU sensors can effectively estimate muscle fatigue during heavy lifting.
    • The model has potential for monitoring and preventing muscle fatigue in occupational settings.
    • Specific biomechanical indicators are crucial for accurate fatigue assessment.