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基于深度学习的体育教育指导模型生成与体质评价人工智能系统。

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

机构信息

School of Physical Education, Huanghuai University, Zhumadian, China.

Life Education Research Center, Henan University, Kaifeng, China.

出版信息

Front Public Health. 2022 Aug 9;10:917053. doi: 10.3389/fpubh.2022.917053. eCollection 2022.

DOI:10.3389/fpubh.2022.917053
PMID:36016903
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9395691/
Abstract

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.

摘要

近年来，中国在人工智能（AI）方面的成就引起了世界的关注，AI 技术已经渗透到各个领域。特别是，AI 技术与体育教育指导和体质评估的深度融合取得了非常显著的进展和成果，提高了人们的生活质量，为人类提供了更多高质量、定制化、个性化的健康管理服务。本研究旨在探讨深度学习在体育教育指导和居民体质分析中的应用模型，以便制定个性化和智能化的运动方案。选择 A、B 两个单位的居民作为研究对象进行体质评估。随后，使用自行设计的问卷对慢性病进行在线调查，并将获得的数据输入深度学习模型进行分析，以获得体质评分结果和运动指导。根据体质评估结果，超重的比例最高（40.4%），其次是脂肪肝（24.3%）和高血脂（20.4%），41-50 岁人群的发病率较高。女性妇科疾病的发病率最高的是痛风（23.0%）和高血脂（20.6%）。经过运动疗法，评分优秀和良好。结论：SQL Server 2005 数据库是存储各种数据和基于知识的规则信息的平台。远程服务器通过浏览器为用户提供访问服务。因此，构建基于规则的推理机制可以实现体质测试数据的采集、体质评估和信息管理，以提高体质。

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