Analysis of health related event detection in big data for physical education training movement detection

Abstract

BACKGROUND: Physical education and training are essential ways to improve the physical quality of the nation, and China has incorporated “building a healthy China” and “fitness for all” into its national development strategy, integrating a strong sports nation into the Chinese dream. OBJECTIVE: The study of digital recording and automated training in sports is of profound value. Motion capture technology can digitally record the training process in a digital physical education training system. At the same time, accurate modeling and calculation can analyze the training effects and give appropriate guidance and feedback. This study develops a new and improved hierarchical K-means algorithm by combining the known classification algorithm K-means with a hierarchical algorithm. METHODS: The performance of the old and new algorithms are compared and then applied to physical education training data to produce clustering results and analysis to reduce the model, which is used to reduce the number of parameters in the model and improve the recognition speed. RESULTS: The experimental results demonstrate that the relevant models proposed in this study achieve an average accuracy of 91.27% and 92.26%, respectively, which is better than a single network model and can effectively use big data for health event detection. CONCLUSION: The empirical results show that the improved model algorithm outperforms the single network model and can detect health events using big data.