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Construction of Sports Machinery Error Model Based on Wireless Communication Technology.

  • Published In: International Journal of High Speed Electronics & Systems, 2025, v. 34, n. 1. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Zhang, Jie; Wang, Li Shui; Zhu, Hao; Hu, AnYun 3 of 3

Abstract

In order to present the evaluation effect of sports on users' health, this paper puts forward the construction of sports machinery error model based on wireless communication technology. A model for evaluating human health by sports is constructed, which consists of data layer, logic layer and display layer. The data layer is used to obtain sports event data, real-time sports data, and health monitoring data, and transmit them to the logic layer. The logic layer fuses human health data and extracts the characteristics of human health information. Combining with wireless communication technology, the characteristics are input into the long-short memory neural network, which outputs the results of sports health pattern recognition after forward and reverse operations, thus realizing the construction of sports machinery error model. The experimental results show that the model can effectively improve the BMI index value of the human body and reduce the maximum loss value, and the output results have higher reliability and fit, faster iteration speed and better performance. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of High Speed Electronics & Systems. 2025/03, Vol. 34, Issue 1, p1
  • Document Type:Article
  • Subject Area:Communication and Mass Media
  • Publication Date:2025
  • ISSN:0129-1564
  • DOI:10.1142/S0129156425401329
  • Accession Number:184145705
  • Copyright Statement:Copyright of International Journal of High Speed Electronics & Systems is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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