Ergonomic postural assessment using a new open-source human pose estimation technology (OpenPose)

Abstract

Observational ergonomic postural assessment methods have been commonly used to evaluate the risks of musculoskeletal disorders. Researchers have proposed semi-automatic methods using Kinect, known for limitations with body occlusions and non-frontal tracking. Meanwhile, new human pose estimation methods have been actively developed, and a popular open-source technology is OpenPose. This study aims to propose the OpenPose-based system for computing joint angles and RULA/REBA scores and validate against the reference motion capture system, and compare its performance to the Kinect-based system. Recordings of 10 participants performing 12 experimental tasks under different conditions: with/without body occlusions and tracked from frontal/non-frontal views were analyzed. OpenPose showed good performance under all task conditions, whereas Kinect performed significantly worse than OpenPose especially at cases with body occlusions or non-frontal tracking. The findings suggested that OpenPose could be a promising technology to measure joint angles and conduct semi-automatic ergonomic postural assessments in the real workspace where the conditions are often non-ideal.