Why the study?
Traditional simulation methods set metabolism as a constant, failing to truly reflect the impact of exercise intensity on dynamic thermal regulation and exercise health.
Does an intelligence exercise health simulation model based on dynamic thermal regulation improve the prediction of health symptoms during exercise compared to traditional constant-metabolism models?
Does an intelligence exercise health simulation model based on dynamic thermal regulation improve the prediction of health symptoms during exercise compared to traditional constant-metabolism models?
A novel computational simulation model incorporating dynamic thermal regulation and heart rate-driven metabolic changes may improve the monitoring and prediction of adverse health events during exercise.
Answers are grounded in this paper and cite their sources.
May support dynamic modeling for exercise symptom prediction; leaves open comparative validation versus constant-metabolism models in humans.
The improvements about the quality of life and human health have been considered important in the development of national and enterprise policies. Participation in a regular exercise program can effectively improve health status and prevent disease. The research of health status monitor and prediction during exercise is benefit to obtain the good exercise affects and avoid some exercise accidents in advance. With the development of computer and AI technologies, it becomes possible to monitor and predict some exercise accidents in advance by computer simulation modeling. In this paper, an intelligence exercise health simulation model based on dynamic thermal regulation is presented, which comprises a dynamic metabolic rate model driven by the change of heart rate to solve the problem that metabolism is set as a constant in the traditional simulation method and can more truly reflect the impact of exercise intensity, and an improved thermal regulation model of human body empowered by dynamic metabolism that is able to simulate physiological phenomena of human body in exercise more accurate. During the numerical simulation processing, some important physiological indicators are calculated to reflect the thermal physiological regulation dynamic change in the exercise. The analysis and monitoring of simulated physiological indicators can help people get healthy exercise and prevent accidents. We design the health prediction module to realize the intelligently prediction of health symptoms in exercise. The effectiveness of the proposed simulation model is verified by experiments.
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Wang et al. (2022) studied this question.
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