Physical Health Data Analysis of Youth Sports Based on Cloud Computing and Gait Perception

To improve the accuracy and efficiency of body data analysis for adolescent sports, a physical health data analysis method of youth sports based on cloud computing and gait perception is proposed. By constructing the cloud computing framework with five layers of data acquisition layer, cloud storage layer, cloud computing layer, data analysis layer and data application layer, and utilizing random forest improved by sampling mode and decision tree feature splitting mode as cloud computing layer, the method excavates the physical health data essence of youth sports under different sports conditions, thereby realizing the physical health data analysis of youth sports. The results show that the proposed method can accurately and quickly evaluate the impact of different types of sports on adolescents’ physical health data. Moreover, its average accuracy rate is 99.22%, and its average training time is 110.14 seconds, which are significantly better than those of logistic regression and LightGBM methods. It provides a reference for scientifically guiding adolescent sports and improving adolescents’ physical health.