Using AI Motion Capture Systems to Capture Race Walking Technology at a Race Scene: A Comparative Experiment

Abstract

Background: This study tested the reliability of the 3D coordinates of human joint points obtained by using an AI motion capture system at a race walking scene. Methods: Using a direct linear transformation (DLT) 3D video recording method, 15 race walking athletes were photographed. We compared the average values, standard deviations, and 95% confidence intervals of the multiple correlation coefficients and differences in the 3D coordinate–time curve of the human joint points that were automatically parsed by the AI motion capture system with those that were manually parsed. Results: Except for the left shoulder y coordinates, left hip y and z coordinates, and left toe tip z coordinates, the multiple correlation coefficients between the curve obtained via the automatic analysis and the average curve obtained via the manual analysis of the other coordinates were greater than 0.90, while the difference between the curve obtained via the automatic analysis and the curve obtained via the manual analysis of the left hand, the left wrist, the left hip, and the left toe was less than 0.025 m. Conclusion: The 3D coordinates of the human joint points obtained via the AI motion capture system were highly similar to the average value of the 3D coordinates obtained via the manual analysis, supporting the use of the AI motion capture system as a highly reliable means to capture the technical motion of race walking in the race walking competition context.