###### 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. AI; motion capture; video analysis; race walking; contrast experiment +
Footnote †: journal: Medical  
Computer vision uses cameras and computers to capture, track, and measure the targets, and it uses AI algorithms to achieve automatic motion recognition [11], breaking through many limitations of traditional motion recognition technologies. Beijing Sport University, Dalian Fast Move Technology Co., Ltd. (Dalian, China), and China Track and Field Association jointly developed an AI motion capture system (Fastmove 3D Motion AI, Dalian Fast Move Technology Co., Ltd., Dalian, China). This system is a neural network model that is used for automatic human motion tracking and pose estimation based on computer vision deep learning technology [12]. The system uses optical flow tracking technology to reduce the image blur interference and background interference caused by motion in the computer system which is tracking a moving human body. After accurately capturing the moving human body, the system uses the convolutional neural network method to conduct in-depth learning by using a large number of parsed training datasets, and it is trained to generate a neural network model which is then used for estimating the human posture and for establishing a probability distribution map of the positions of joint points within the outer contours of the human body. In this way, the human posture estimation neural network model can identify human joint points in different movements, and it can automatically read the pixel coordinates of the joint points [13]. After the 3D coordinate data of the joint points are collected, the model-driven calculation function is used to automatically calculate the human kinematic data. At present, the kinematic analysis of race walking technology mainly relies on the manual analysis of race walking videos. Although the analysis results can be guaranteed in terms of their accuracy, the analysis itself takes a substantial amount of time. The changes in race walking technology during competition and training cannot be fed back to the coach in a timely manner, thus losing the coach's best opportunity to provide technical guidance to athletes. If Fastmove 3D Motion AI can be applied to the analysis of race walking technology, the problem of lagging feedback in race walking technology analysis will be solved.