Personalized Sports Training Strategy Optimization Method Based on Multi-Task Deep Deterministic Policy Gradient
Year: 2025
Type: conference-paper
Abstract: With the continuous development of big data technology, its application in the field of sports health management is becoming more and more in-depth. Compared with traditional methods, big data has the advantages of large capacity, multiple types, fast processing and high precision, providing new technical support for the precision of health management. In the past, our methods of collecting and analyzing health data have been difficult to meet the current needs of people for personalized and intelligent services. Although traditional methods can process health data to a certain extent, they often seem to be unable to cope with large-scale data processing, real-time monitoring and the generation of personalized suggestions. With the help of big data technology, we can now realize the real-time collection, processing and analysis of large-scale health data, bringing new ideas and methods to improve the health level of the whole people. However, there are still many problems in the current sports health management system, especially in scientific guidance and personalized strategies. Many people lack accurate guidance when exercising, which not only affects the effect of exercise, but also may increase the risk of injury. In response to these problems, we proposed a set of intelligent sports health management system based on big data. The system integrates sensor networks, Internet of Things devices and cloud computing platforms, and can automatically complete the collection, collation, storage and dynamic analysis of health data. At the same time, it can accurately monitor personal health status and provide personalized exercise suggestions based on the analysis results. Compared with traditional methods, this system has obvious advantages in providing personalized guidance and preventing sports injuries. It can effectively make up for the shortcomings of existing methods and bring people more scientific and efficient health management solutions.
Authors: Rongdan Chen, Yu Zhang
Institution: Tianjin University of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4412055673