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Estimating Trunk Angle Kinematics During Lifting Using a Computationally Efficient Computer Vision Method.

Runyu L Greene1, Ming-Lun Lu2, Menekse Salar Barim2

  • 15228 University of Wisconsin-Madison, Wisconsin, USA.

Human Factors
|September 25, 2020
PubMed
Summary
This summary is machine-generated.

A new computer vision method estimates trunk flexion, speed, and acceleration during lifting using simple video features. This approach aids in workplace lifting risk assessments, potentially via smartphones.

Keywords:
job risk assessmentkinematicslow backmanual materials handlingwork physiology

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Area of Science:

  • Biomechanics and Ergonomics
  • Computer Vision Applications
  • Occupational Health

Background:

  • Trunk kinematics are crucial risk factors for lower back pain.
  • Accurate measurement of trunk kinematics during lifting is challenging for practitioners.
  • Existing methods for lifting risk assessment lack accessibility and ease of use.

Purpose of the Study:

  • To develop and validate a computer vision method for estimating trunk flexion angle, angular speed, and angular acceleration during lifting tasks.
  • To assess the feasibility of using simple image features for kinematic analysis.
  • To provide a tool for objective and automated lifting risk assessment.

Main Methods:

  • Systematic generation of lifting postures using 3DSSPP software.
  • Application of bounding box regression models to estimate trunk angles from generated and real-world lifting videos.
  • Validation against a laboratory-grade motion capture system using 216 lifts.

Main Results:

  • The computer vision method demonstrated a mean absolute difference of 14.7° compared to motion capture measurements.
  • A significant linear relationship (R² = .80, p < .001) was found between predicted and measured trunk angles.
  • The kinematics model achieved a low training error of 2.3°.

Conclusions:

  • Computer vision, using bounding box features, can indirectly estimate trunk angle and kinematics during lifting.
  • While precision is limited, the method offers a practical approach to assessing lifting biomechanics.
  • The developed method shows potential for implementation on mobile devices for workplace safety.