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Detecting subject-specific fatigue-related changes in lifting kinematics using a machine learning approach.

Sheldon J Hawley1, Andrew Hamilton-Wright2, Steven L Fischer1

  • 1Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, Canada.

Ergonomics
|April 4, 2022
PubMed
Summary

This study shows that an outlier detection method using one-class support vector machines (OCSVM) can objectively identify fatigue during repetitive lifting tasks. This subject-specific approach correlates kinematic changes with perceived exertion, aiding in fatigue management.

Keywords:
Machine learningbiomechanicsmanual materials handlingpattern recognitionsupport vector machine

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Area of Science:

  • Biomechanics
  • Ergonomics
  • Machine Learning

Background:

  • Fatigue responses during lifting tasks are highly individual, necessitating subject-specific detection methods.
  • Current fatigue assessment often relies on subjective measures, lacking objectivity in workplace settings.
  • One-class support vector machines (OCSVM) offer a potential objective approach for classifying kinematic changes associated with fatigue.

Purpose of the Study:

  • To evaluate the efficacy of a subject-specific OCSVM approach for objective fatigue detection during repetitive lifting.
  • To determine if changes in lifting kinematics, identified as outliers by OCSVM, correlate with self-reported fatigue levels.

Main Methods:

  • Participants performed a repetitive lifting protocol while motion capture recorded kinematic data.
  • Subject-specific OCSVM models were trained using the initial lifting motions (first 35%) of each participant.
  • Subsequent lifts were classified as outliers against the trained OCSVM decision boundaries, and the percentage of outliers was correlated with the rating of perceived exertion (RPE).

Main Results:

  • A significant positive correlation was observed between the percentage of outlier lifts and RPE in participants who exhibited fatigue.
  • No significant correlation was found for participants who did not report increased fatigue.
  • The OCSVM successfully identified changes in lifting kinematics indicative of fatigue.

Conclusions:

  • An OCSVM-based outlier detection tool shows prospective efficacy for subject-specific, objective fatigue detection in repetitive lifting.
  • This machine learning approach can identify changes in movement patterns associated with increased self-reported fatigue.
  • The findings support the development of objective fatigue monitoring systems for occupational safety and performance optimization.