A Physiological-Signal-Based Thermal Sensation Model for Indoor Environment Thermal Comfort Evaluation

International Journal of Environmental Research and Public Health

Ing-Jer Huang¹  Shin-Yu Wu¹  Shih-Lung Pao¹  Wen-Lan Wu²  Lan-Yuen Guo²  Jing-Min Liang²  Shy-Her Nian³  Yang-Guang Liu³ 

[1] Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan;Department of Sports Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan;Green Energy & Environmental Laboratories, Industrial Technology Research Institute, Hsinchu 31040, Taiwan;

关键词: thermal sensation;    thermal comfort;    PMV (predicted mean vote);    sensation modeling;    personalized thermal comfort strategy;    EMG;   

DOI  :  10.3390/ijerph19127292

来源: DOAJ

【 摘 要 】

Traditional heating, ventilation, and air conditioning (HVAC) control systems rely mostly on static models, such as Fanger’s predicted mean vote (PMV) to predict human thermal comfort in indoor environments. Such models consider environmental parameters, such as room temperature, humidity, etc., and indirect human factors, such as metabolic rate, clothing, etc., which do not necessarily reflect the actual human thermal comfort. Therefore, as electronic sensor devices have become widely used, we propose to develop a thermal sensation (TS) model that takes in humans’ physiological signals for consideration in addition to the environment parameters. We conduct climate chamber experiments to collect physiological signals and personal TS under different environments. The collected physiological signals are ECG, EEG, EMG, GSR, and body temperatures. As a preliminary study, we conducted experiments on young subjects under static behaviors by controlling the room temperature, fan speed, and humidity. The results show that our physiological-signal-based TS model performs much better than the PMV model, with average RMSEs 0.75 vs. 1.07 (lower is better) and R² 0.77 vs. 0.43 (higher is better), respectively, meaning that our model prediction has higher accuracy and better explainability. The experiments also ranked the importance of physiological signals (as EMG, body temperature, ECG, and EEG, in descending order) so they can be selectively adopted according to the feasibility of signal collection in different application scenarios. This study demonstrates the usefulness of physiological signals in TS prediction and motivates further thorough research on wider scenarios, such as ages, health condition, static/motion/sports behaviors, etc.

【 授权许可】

Unknown