Analysis of Simulated Track and Field Starting Motion Based on Spectral Sensors and Motion Capture Algorithms

Abstract

Abstract In track and field movements, effective starting movements can improve an athlete's explosive power and acceleration, and enhance their competitive level. The existing methods are mainly based on manual visual inspection and two-dimensional image analysis. Therefore, this study proposes a new method based on spectral sensors and motion capture algorithms to better analyze starting movements. The study used spectral sensors and motion capture systems to collect data on starting movements. Spectral sensors measure the movement status of various parts of the athlete's body in real-time, accurately capturing the movement changes during the starting process. In order to analyze the key links and motion trajectories of the starting motion, the motion capture algorithm processes the data collected by sensors in real time, and obtains key parameters based on changes in body parts to analyze the details of the starting motion. By analyzing the collected data, a quantitative evaluation and feedback on the starting movement can be obtained. These evaluation results can help athletes and coaches better understand the advantages and improvement space of the starting movement, and provide them with targeted training suggestions.