Sports Mental Health Monitoring Based on Multiobjective Evolutionary Algorithms in the Internet of Things

Abstract

Abstract With the increasing popularity of sports such as football and basketball, many policy reforms are striving to strengthen the education of sports talents in China. The release of relevant policies can further promote the development of sports and enable more and more people to promote physical and mental health through sports activities. The emergence of the Internet of Things, especially the emergence of intelligent wearable devices, has brought many conveniences to the long-term detection and analysis of human health and motion parameters. By measuring human health and sports information, it is possible to quantitatively monitor the amount of exercise and physical health level of athletes, in order to achieve more effective training effects. In addition, for complex multi-objective problems, multi-objective evolutionary algorithms can effectively solve problems that are difficult to handle, suppress, and balance targets. Therefore, this article uses a multi-objective evolutionary algorithm to complete mental health monitoring based on the Internet of Things. It monitors the impact of sports on students' mental health, compares their mental health before and after the experiment, and analyzes the data changes before and after the experiment to summarize the impact of sports on students' mental health. The main characteristics of multi-objective evolutionary algorithm are excellent convergence and diversity. This article uses its main characteristics to real-time monitor the mental health level of college students during exercise through the Internet of Things, opening up a new way to improve their mental health.