An artificial intelligence evaluation system found that among residents, the highest proportion of health issues was overweight (40.4%), followed by fatty liver (24.3%) and hyperlipidemia (20.4%).
Cross-Sectional (n=100)
No
An AI-based deep learning model can evaluate physical fitness and provide personalized sports education guidance based on physical test data and chronic disease surveys.
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.
Li et al. (Tue,) conducted a cross-sectional in Physical fitness and chronic diseases (n=100). Artificial Intelligence System (BPNN algorithm) was evaluated on Overweight prevalence. An artificial intelligence evaluation system found that among residents, the highest proportion of health issues was overweight (40.4%), followed by fatty liver (24.3%) and hyperlipidemia (20.4%).