Estimating Trunk Angles During Lifting Using Computer Vision Bounding Boxes *

Abstract

We previously developed a method for classifying lifting postures using dimensions of a rectangular bounding box drawn tightly around the subject for a single camera view that is more tolerable of conditions encountered in industrial settings than high precision tracking and can be practically implemented on a smart hand-held device. This study explores the use of simple bounding box dimensions to predict trunk angle while lifting. Mannequin poses were generated using the Michigan 3DSSPP software for 105 postures across six anthropometries. A regression model for predicting trunk angle was created (adjusted R²=0.91, p<.001). Predicted trunk angles compared against measured 3D motion capture for five participants (N=180 lifts) had a mean error of 15.85° (SE=0.63°, R² = 0.80). This algorithm should be useful for calculating trunk kinematic properties that are associated with increased risks of low-back disorders including trunk speed and acceleration using successive video frames of predicted trunk flexion angles.