A Physiological-Signal-Based Thermal Sensation Model for Indoor Environment Thermal Comfort Evaluation
| Autor: | Shih-Lung Pao, Shin-Yu Wu, Jing-Min Liang, Ing-Jer Huang, Lan-Yuen Guo, Wen-Lan Wu, Yang-Guang Liu, Shy-Her Nian |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Heating
Health Toxicology and Mutagenesis thermal sensation thermal comfort PMV (predicted mean vote) sensation modeling personalized thermal comfort strategy EMG ECG EEG GSR body temperature Temperature Public Health Environmental and Occupational Health Humans Air Conditioning Humidity Thermosensing Ventilation |
| Zdroj: | International Journal of Environmental Research and Public Health; Volume 19; Issue 12; Pages: 7292 |
| ISSN: | 1660-4601 |
| DOI: | 10.3390/ijerph19127292 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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