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Feature Detection and Biomechanical Analysis to Objectively Identify High Exposure Movement Strategies When Performing the EPIC Lift Capacity test

Abstract

Purpose The Epic Lift Capacity (ELC) test is used to determine a worker’s maximum lifting capacity. In the ELC test, maximum lifting capacity is often determined as the maximum weight lifted without exhibiting a visually appraised “high-risk workstyle.” However, the criteria for evaluating lifting mechanics have limited justification. This study applies feature detection and biomechanical analysis to motion capture data obtained while participants performed the ELC test to objectively identify aspects of movement that may help define “high-risk workstyle”. Method In this cross-sectional study, 24 participants completed the ELC test. We applied Principal Component Analysis, as a feature detection approach, and biomechanical analysis to motion capture data to objectively identify movement features related to biomechanical exposure on the low back and shoulders. Principal component scores were compared between high and low exposure trials (relative to median exposure) to determine if features of movement differed. Features were interpreted using single component reconstructions of principal components. Results Statistical testing showed that low exposure lifts and lowers maintained the body closer to the load, exhibited squat-like movement (greater knee flexion, wider base of support), and remained closer to neutral posture at the low back (less forward flexion and axial twist) and shoulder (less flexion and abduction). Conclusions Use of feature detection and biomechanical analyses revealed movement features related to biomechanical exposure at the low back and shoulders. The objectively identified criteria could augment the existing scoring criteria for ELC test technique assessment. In the future, such features can inform the design of classifiers to objectively identify “high-risk workstyle” in real-time.

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Acknowledgements

This work was funded by Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant (RGPIN-2018-04483) and Ontario Ministry of Research and Innovation Early Research Award (ER16-12-163) held by S. Fischer.

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Correspondence to Steven L. Fischer.

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Daniel P. Armstrong, Aleksandra R. Budarick, Claragh E.E. Pegg, Ryan B. Graham, and Steven L. Fischer declare that they have no conflict of interest.

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All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000 (5). Informed consent was obtained from all patients for being included in the study.

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Armstrong, D.P., Budarick, A.R., Pegg, C.E.E. et al. Feature Detection and Biomechanical Analysis to Objectively Identify High Exposure Movement Strategies When Performing the EPIC Lift Capacity test. J Occup Rehabil 31, 50–62 (2021). https://doi.org/10.1007/s10926-020-09890-2

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  • DOI: https://doi.org/10.1007/s10926-020-09890-2

Keywords

  • Automated pattern recognition
  • Lifting
  • Work capacity evaluation
  • Kinematics
  • Kinetics