Data analysis of physical recovery and injury prevention in sports teaching based on wearable devices

Abstract

Athletes can also cause damage to some parts of their body during training, so specialized preparation activities should be carried out before athlete training to reduce the damage caused to the athlete's body, allowing the stressed parts to move and distribute the load. Excessive recovery has a significant effect on improving the performance level of the athletes studied and preventing sports injuries. This article studies the data analysis of body recovery and injury prevention in physical education teaching based on wearable devices. Real time collection of students' exercise data, including indicators such as exercise volume, heart rate, steps, distance, etc., by wearing wearable devices. By using Internet of Things technology to transmit data to cloud servers, data analysis and mining techniques are used to process the data and study issues related to body recovery and injury prevention. Specifically, this article adopts methods such as time series analysis, machine learning algorithms, and artificial neural networks to analyze the relationship between exercise data and body recovery and injury prevention, providing scientific guidance and support for physical education teaching. This method can monitor students' exercise data in real-time, predict the risk of physical recovery and injury, and provide corresponding prevention and guidance suggestions.