Sport School of Jiangsu Normal University, Xuzhou 221116, Jiangsu, China.
Sport School of Jiangsu Normal University, Xuzhou 221116, Jiangsu, China.
Prev Med. 2023 Aug;173:107589. doi: 10.1016/j.ypmed.2023.107589. Epub 2023 Jun 24.
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.
运动员在训练过程中也会对身体的某些部位造成损伤，因此在运动员训练前应进行专门的准备活动，以减少对运动员身体的损伤，使受压部位活动并分担负荷。过度恢复对提高运动员的运动成绩和预防运动损伤有显著效果。本文基于可穿戴设备研究体育教学中身体恢复和损伤预防的数据。通过佩戴可穿戴设备实时采集学生的运动数据，包括运动强度、心率、步数、距离等指标。利用物联网技术将数据传输到云服务器，使用数据分析和挖掘技术处理数据，研究与身体恢复和损伤预防相关的问题。具体来说，本文采用时间序列分析、机器学习算法和人工神经网络等方法分析运动数据与身体恢复和损伤预防之间的关系，为体育教学提供科学指导和支持。这种方法可以实时监测学生的运动数据，预测身体恢复和受伤的风险，并提供相应的预防和指导建议。
