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

Abstract

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.

Clothing Thermophysiological Comfort: A Textile Science Perspective

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A Review on Human Comfort Factors, Measurements, and Improvements in Human–Robot Collaboration

“…Zhai et al [ 81 ] found that the patterns of temperature slope provide more meaningful information than the mean value in terms of emotion classification accuracy. Pao et al [ 82 ] proposed a thermal sensation prediction model by adopting physiological features including body temperature, EDA, EEG, and ECG. The accuracy results indicate that this new model performs better than the predicted mean vote (PMV) model.…”

Influence of the Thermal Environment on Occupational Health and Safety in Automotive Industry: A Case Study

“…In recent years, the number of studies on the effects of the indoor thermal environment on human physical and mental health has increased. The studies focused on different aspects of the theme, such as assessing the indoor thermal environment in specific locations (classrooms, workplaces) [ 6 , 7 ], using electronic sensors for monitoring and improving the thermal sensation [ 8 ], using IT instruments for the modelling and prediction of thermal comfort [ 9 , 10 , 11 ], or the effects of heat-related illness on mental health [ 12 ].…”