Research On Intelligent Body Measurement Algorithm Based on AI Attitude Recognition

Abstract

To solve the problems of difficult quantitative analysis of standing long jump movements, unclear factors affecting performance, and lack of personalized training programs, this paper conducts research based on AI human posture estimation technology to obtain key node coordinate data of athletes. To address the challenges of data noise and action stage segmentation, sliding window smoothing is used to process data, and dynamic ground baselines are constructed by combining foot nodes. Weighted fusion features are used to accurately determine takeoff and landing times, and the hovering process is quantified from dimensions such as center of gravity displacement and joint angles; To meet the needs of performance prediction and training optimization, a prediction model is constructed based on gradient boosting regression. The simulation results show a high accuracy in predicting the actual long jump performance of athletes. In the field of sports, the research findings of this article can be extended to other track and field events such as high jump, throwing, and sprinting, as well as the analysis of technical movements in ball sports such as basketball and football.