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Mobile Information Systems
Volume 2016 (2016), Article ID 8586493, 16 pages
Research Article

A Novel Exercise Thermophysiology Comfort Prediction Model with Fuzzy Logic

1School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China
2School of Computer and Information Security, Guilin University of Electronic Technology, Guilin, China
3School of Computer, South China Normal University, Guangzhou, China

Received 20 September 2016; Accepted 8 November 2016

Academic Editor: Qingchen Zhang

Copyright © 2016 Nan Jia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.