EXPLORATORY RESEARCH AND ANALYSIS OF THE PHYSICAL FITNESS TEST SYSTEM ON MOTION CAPTURE AND BODY TRACKING

Author(s): YANG, P.C., CHIU, Y.2,3, CHUANG, Y.4, CHEN, I.3, Institution: CHANG JUNG CHRISTIAN UNIVERSITY, Country: TAIWAN, Abstract-ID: 1517

INTRODUCTION:
Physical fitness encompasses the bodys capacity to adjust comprehensively. More workforce and material resources must be put into the fitness assessment, which can lead to inconsistent data quality due to different examiners or assistants. The data collected from the assessment tends to be fragmented or singular, needing continuous process records and analysis of dynamic motion. With the advancement of science and technology and the growing trend of sports technology, scientific data is collected and measured conveniently through innovative computer vision technology.
METHODS:
This study aims to establish the necessary technological framework for motion capture and body tracking to analyze the landmark data of human bodies in a video and to develop computer vision technology to design and develop the physical fitness detection system for college students. This research designed an image data collection environment, developed a computer vision-based physical fitness test system using Googles MediaPipe framework to measure and validate data of physical fitness testing items, and enhanced Physical Fitness Testing procedures.
RESULTS:
The dimensions of the experimental space are 300 centimeters in length, 380 centimeters in width, and 250 centimeters in height. We analyzed data from 6 participants. The data collection sessions were conducted individually and were stored for later analysis. The preliminary result shows that the sit-ups, sit and reach, and standing long jump is high as large, more than 0.9; the correlation via logistic regression models suggests that the relationship between the traditional method and the computer vision-based technology beliefs is very strong.
CONCLUSION:
AI imaging technology brings benefits to physical fitness testing. The system can capture human body motion via pose detection and pose tracking. The studys overall architecture is feasible and practical.