ArticleFrontiers in public health2022
The Artificial Intelligence System for the Generation of Sports Education Guidance Model and Physical Fitness Evaluation Under Deep Learning.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed, 28 citations in OpenAlex.
- Perceptions of Artificial Intelligence in Gynecologic Oncology Education: Insights from a Qualitative Study Using Semi-Structured Interviews.Journal of cancer education : the official journal of the American Association for Cancer Education · 2026Article
- Cognitive status of nursing postgraduates toward Generative Artificial Intelligence: a qualitative study based on the UTAUT framework.BMC nursing · 2026Article
- Associations between generative AI use frequency, technology acceptance, attitudes toward AI, and reported learning preference patterns among students in physical education classes.Frontiers in sports and active living · 2026Article
- Deep learning for genomic insights into athletic performance in sports education.Frontiers in genetics · 2026Article
- Intelligent optimization of track and field teaching using machine learning and wearable sensors.Scientific reports · 2025Article
- Precision management in chronic disease: An AI empowered perspective on medicine-engineering crossover.iScience · 2025Review
- The Paradox of AI Empowerment in Primary School Physical Education: Why Technology May Hinder, Not Help, Teaching Efficiency.Behavioral sciences (Basel, Switzerland) · 2025Article
- The role of artificial intelligence in enhancing sports education and public health in higher education: innovations in teaching models, evaluation systems, and personalized training.Frontiers in public health · 2025Article
- The analysis of aerobics intelligent fitness system for neurorobotics based on big data and machine learning.Heliyon · 2024Article
- Artificial intelligence in physical education: comprehensive review and future teacher training strategies.Frontiers in public health · 2024Review
- Smart Devices for Health and Wellness Applied to Tele-Exercise: An Overview of New Trends and Technologies Such as IoT and AI.Healthcare (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.