Thermal radiation optical motion capture based on depth camera perception for basketball fatigue detection simulation

Abstract

Traditional fatigue detection methods usually rely on physiological indicators and subjective evaluation, which has certain limitations. Because of its non-contact and high sensitivity, thermal radiation optical motion capture technology has gradually become a research hotspot in fatigue detection. This paper aims to explore the application of thermal radiation optical motion capture technology based on depth camera perception in the fatigue detection of basketball players, so as to provide a new method for improving sports load management and reducing the risk of sports injuries. In this paper, depth camera and thermal imaging technology are used to collect the thermal radiation information of basketball players under different fatigue states. Through data preprocessing and feature extraction, a fatigue recognition model based on machine learning was constructed. In the experiment, the athletes completed standardized basketball training sessions, during which the heat radiation data was recorded in real time and compared with physiological indicators (such as heart rate and breathing rate). The experimental results show that the fatigue detection model based on thermal radiation optical motion capture has higher accuracy than the traditional method, and can reflect the change of fatigue state of athletes in time. There is a significant correlation between thermal imaging data and physiological indicators, which verifies the effectiveness of the method. Thermal radiation optical motion capture technology based on depth camera perception provides an effective solution for fatigue detection of basketball players.