A Novel Exercise Thermophysiology Comfort Prediction Model with Fuzzy Logic

Abstract

Participation in a regular exercise program can improve health status and contribute to an increase in life expectancy. However, exercise accidents like dehydration, exertional heatstroke, syncope, and even sudden death exist. If these accidents can be analyzed or predicted before they happen, it will be beneficial to alleviate or avoid uncomfortable or unacceptable human disease. Therefore, an exercise thermophysiology comfort prediction model is needed. In this paper, coupling the thermal interactions among human body, clothing, and environment (HCE) as well as the human body physiological properties, a human thermophysiology regulatory model is designed to enhance the human thermophysiology simulation in the HCE system. Some important thermal and physiological performances can be simulated. According to the simulation results, a human exercise thermophysiology comfort prediction method based on fuzzy inference system is proposed. The experiment results show that there is the same prediction trend between the experiment result and simulation result about thermophysiology comfort. At last, a mobile application platform for human exercise comfort prediction is designed and implemented.

Learning curve improvement model for sportswear comfort and sports functionality analysis

“…Therefore, improving the comfort and sports functionality of sports apparel has become an important research direction in the design and production of sports apparel [6]. However, it is still a challenge to accurately evaluate and optimize the comfort and sports functionality of sportswear during the design and production of sportswear [7][8]. Traditional design methods mainly rely on the designer's experience and intuition, making it difficult to accurately predict sportswear performance [9].…”

An Exercise Health Simulation Method Based on Integrated Human Thermophysiological Model

“…This may also lead to an improvement in developing training protocols for athletes and more efficient weight loss protocols for the obese and in facilitating evaluation of physical fitness and health of individuals [ 33 ]. To clarify, we noted the importance of computer simulation technique in the study of human sports and proposed a method to assess human exercise comfort in 2016 [ 34 ]. Different from the work of this paper, the previous one employs human physiological model to obtain physiological indicators and defines a set of fuzzy rules to measure the human comfort, while this work applies the obtained physiological indicators as input of a complicated fuzzy finite state machine and then quantifies the human exercise health status.…”