Developing a computer-vision model to estimate anatomical joint coordinates during manual lifting tasks

Abstract

This study developed a Computer-Vision based anatomical joint coordinates estimation model to predict the 3D joint coordinates with the help of Artificial Intelligence image recognition technology during manual lifting tasks based on single camera video inputs. The workflow of the proposed Computer-Vision model includes 2D joint detection and 3D joint reconstruction. The 3D joint error is calculated based on the Euclidean distance between the predicted 3D joint coordinates from the CV-based method and the corresponding joint coordinates of the ground truth from the Visual 3D TM skeletal model. The results indicated that the floor to shoulder lifting height path induced a greater 3D joint error than the floor to knuckle and knuckle to shoulder lifting height paths (p-value = 0.01). The 3D joint error of the hand was the largest than the other estimated joints. This study verified that the proposed Computer-Vision model could predict 3D joint points. Therefore, while the marker-based motion tracking system is inapplicable, the model can be used as an alternative solution for predicting lifting motion.

Keywords: Artificial Intelligence image recognition technology, Convolutional Neural Network, 3D human joint coordinate estimation, Lifting.

DOI: 10.54941/ahfe1002615