Athleteʼs Physical Fitness Prediction Model Algorithm and Index Optimization Analysis under the Environment of AI. (25th February 2021)

• Record Type: Journal Article
• Authors: Zhao, Liqiu; Zhao, Yuexi; Wang, Xiaodong
• Other Names: Tsai Sang-Bing Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2021(2021)
• Publication Date: 2021-02-25
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2021/6680629
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : With the rapid progress of network technology and computers, the Internet of Things has slowly entered peopleʼs lives and work. The Internet of Things can bring a lot of convenience to peopleʼs lives and work. People have been living in a networked era, and communications, computers, and network technologies are changing the entire human race and society. The extensive application of databases and computer networks, coupled with the use of advanced automatic data collection tools, has dramatically increased the amount of data that people have. There are many important information hidden behind the surge of data, and people hope to conduct higher-level analysis on it in order to make better use of these data. This article mainly introduces the prediction model algorithm and index optimization analysis of athletesʼ physical fitness under the Internet of things environment. This paper proposes an algorithm and index optimization method for the athletesʼ physical fitness prediction model in the Internet of Things environment, which is used to conduct athletesʼ fitness prediction model algorithm and index optimization experiments in the Internet of Things environment, and designs steps for athletesʼ physical fitness prediction in the Internet of Things environment to lay a solid foundation for related applications of athlete index optimization. The experimental results in this article show that the prediction accuracy rate of the professional group with the athleteʼs physical fitness prediction model and index optimization under the Internet of Things environment is higher than that of the control group, with a difference p < 0.001 .