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The Artificial Intelligence System for the Generation of Sports Education Guidance Model and Physical Fitness Evaluation Under Deep Learning

In recent years, China's achievements in artificial intelligence (AI) have attracted the attention of the world, and AI technology has penetrated into all walks of life. In particular, the in-depth integration of AI technology with sports education guidance and physical fitness evaluation has a...

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Autores principales: Li, Yuanqing, Li, Xiangliang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9395691/
https://www.ncbi.nlm.nih.gov/pubmed/36016903
http://dx.doi.org/10.3389/fpubh.2022.917053
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author Li, Yuanqing
Li, Xiangliang
author_facet Li, Yuanqing
Li, Xiangliang
author_sort Li, Yuanqing
collection PubMed
description In recent years, China's achievements in artificial intelligence (AI) have attracted the attention of the world, and AI technology has penetrated into all walks of life. In particular, the in-depth integration of AI technology with sports education guidance and physical fitness evaluation has achieved very significant progress and results, which has improved the quality of life of people and provided more high-quality, customized, and personalized health management services for human beings. This study aimed to explore the application model of deep learning in sports education and guidance and in the analysis of the residents' physical fitness, so as to formulate a personalized and intelligent exercise program. The residents of A and B units are selected as the research object to evaluate the physical fitness. Subsequently, the self-designed questionnaire is used to survey the chronic disease online, and the acquired data are put into a deep learning model for the analysis to obtain the physique scoring results and exercise guidance. According to the results of physical fitness evaluation, the proportion of overweight was the highest (40.4%), followed by fatty liver (24.3%) and hyperlipidemia (20.4%), showing high incidence in people aged 41–50 years. The highest incidence of female gynecological diseases was gout (23.0%) and hyperlipidemia (20.6%). After exercise therapy, the scores were excellent and good. Conclusions: The database SQL Server 2005 was a platform for storing all kinds of data and knowledge-based rule information. The user's access service was provided by the remote server via the browser. Therefore, building a rule-based reasoning mechanism can realize physical test data collection, physical fitness evaluation, and information management for improving physical fitness.
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spelling pubmed-93956912022-08-24 The Artificial Intelligence System for the Generation of Sports Education Guidance Model and Physical Fitness Evaluation Under Deep Learning Li, Yuanqing Li, Xiangliang Front Public Health Public Health In recent years, China's achievements in artificial intelligence (AI) have attracted the attention of the world, and AI technology has penetrated into all walks of life. In particular, the in-depth integration of AI technology with sports education guidance and physical fitness evaluation has achieved very significant progress and results, which has improved the quality of life of people and provided more high-quality, customized, and personalized health management services for human beings. This study aimed to explore the application model of deep learning in sports education and guidance and in the analysis of the residents' physical fitness, so as to formulate a personalized and intelligent exercise program. The residents of A and B units are selected as the research object to evaluate the physical fitness. Subsequently, the self-designed questionnaire is used to survey the chronic disease online, and the acquired data are put into a deep learning model for the analysis to obtain the physique scoring results and exercise guidance. According to the results of physical fitness evaluation, the proportion of overweight was the highest (40.4%), followed by fatty liver (24.3%) and hyperlipidemia (20.4%), showing high incidence in people aged 41–50 years. The highest incidence of female gynecological diseases was gout (23.0%) and hyperlipidemia (20.6%). After exercise therapy, the scores were excellent and good. Conclusions: The database SQL Server 2005 was a platform for storing all kinds of data and knowledge-based rule information. The user's access service was provided by the remote server via the browser. Therefore, building a rule-based reasoning mechanism can realize physical test data collection, physical fitness evaluation, and information management for improving physical fitness. Frontiers Media S.A. 2022-08-09 /pmc/articles/PMC9395691/ /pubmed/36016903 http://dx.doi.org/10.3389/fpubh.2022.917053 Text en Copyright © 2022 Li and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Li, Yuanqing
Li, Xiangliang
The Artificial Intelligence System for the Generation of Sports Education Guidance Model and Physical Fitness Evaluation Under Deep Learning
title The Artificial Intelligence System for the Generation of Sports Education Guidance Model and Physical Fitness Evaluation Under Deep Learning
title_full The Artificial Intelligence System for the Generation of Sports Education Guidance Model and Physical Fitness Evaluation Under Deep Learning
title_fullStr The Artificial Intelligence System for the Generation of Sports Education Guidance Model and Physical Fitness Evaluation Under Deep Learning
title_full_unstemmed The Artificial Intelligence System for the Generation of Sports Education Guidance Model and Physical Fitness Evaluation Under Deep Learning
title_short The Artificial Intelligence System for the Generation of Sports Education Guidance Model and Physical Fitness Evaluation Under Deep Learning
title_sort artificial intelligence system for the generation of sports education guidance model and physical fitness evaluation under deep learning
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9395691/
https://www.ncbi.nlm.nih.gov/pubmed/36016903
http://dx.doi.org/10.3389/fpubh.2022.917053
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