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1. Development of the data-driven thermal satisfaction prediction model as a function of human physiological responses in a built environment

   Choi, J. ; Yeom, D. ( January 2019 , Building and environment)

   The purpose of this study is to investigate and determine the relationship between occupants’ thermal satisfaction and physiological responses in an office environment, and to estimate their thermal satisfaction level via human physiological signals. This study adapted the heart rate and seven local body skin temperatures as physiological signals, as well as human factors (gender, age, BMI), to determine establish a thermal satisfaction prediction model by combining human factors and physiological signals. The results revealed significant correlations between overall thermal satisfaction levels and local body skin temperatures, as well as heart rates. The heart rates showed a negative correlation with overall thermal satisfaction, and the skin temperature of the forehead, arm, wrist (back and front), chest, and belly also revealed a significant correlation with the thermal satisfaction levels of the study participants. This study also determined the order and priority of local skin temperatures (as well as gender and BMI) by their impact on thermal satisfaction. Considering all human physiological factors and practical application of the results, the local skin temperatures of the forehead, wrist (back), and gender demonstrated 88.52% accuracy for estimating thermal satisfaction, which provided significant validation for practical use of this procedure. 

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2. Heat Flux Sensing for Machine-Learning-Based Personal Thermal Comfort Modeling

   https://doi.org/10.3390/s19173691  

   Jung, Wooyoung ; Jazizadeh, Farrokh ; Diller, Thomas E. ( September 2019 , Sensors)

   null (Ed.)

   In recent years, physiological features have gained more attention in developing models of personal thermal comfort for improved and accurate adaptive operation of Human-In-The-Loop (HITL) Heating, Ventilation, and Air-Conditioning (HVAC) systems. Pursuing the identification of effective physiological sensing systems for enhancing flexibility of human-centered and distributed control, using machine learning algorithms, we have investigated how heat flux sensing could improve personal thermal comfort inference under transient ambient conditions. We have explored the variations of heat exchange rates of facial and wrist skin. These areas are often exposed in indoor environments and contribute to the thermoregulation mechanism through skin heat exchange, which we have coupled with variations of skin and ambient temperatures for inference of personal thermal preferences. Adopting an experimental and data analysis methodology, we have evaluated the modeling of personal thermal preference of 18 human subjects for well-known classifiers using different scenarios of learning. The experimental measurements have revealed the differences in personal thermal preferences and how they are reflected in physiological variables. Further, we have shown that heat exchange rates have high potential in improving the performance of personal inference models even compared to the use of skin temperature. 

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3. Vision-based Thermal Comfort Quantification for HVAC Control

   Jung, W. ; Jazizadeh, F. ( June 2018 , Building and environment)

   This study presents a vision-based approach that employs RGB video images as the sole source for inferring thermoregulation states in the human body in response to thermal condition variations in indoor environments. The primary objective is to contribute to our envisioned thermoregulation-based HVAC control that leverages actual thermal demands from end-users’ thermoregulation states for increased energy efficiency. Given that the envisioned control system calls for measurement techniques under four constraints of non-intrusiveness, applicability, sensitivity, and ubiquity (i.e., feasibility and scalability), this study investigated the potentials of ubiquitously obtainable RGB video images (through webcams or smartphones). Using photoplethysmography(PPG), a well-known optical technique for measuring blood volume changes in the microvascular bed of skin, we have leveraged the mechanism of blood flow control to the skin surface (blood vessels' dilation and constriction)for heat dissipation regulations, reflected in PPG signal's amplitude. Given the subtle variations of PPG signals and their susceptibility to noise, we proposed a framework that uses a combination of independent component analysis and adaptive filtering to reduce unwanted and in-band artifacts while preserving the amplitude information of PPG signals that indicates thermophysiological states. The framework was experimentally evaluated using transient thermal conditions to account for applicability and sensitivity attributes. Therefore, without considering an acclimation time for stabilized thermoregulation states, human subjects were exposed to varying temperatures (∼20–30 °C) while reporting their thermal sensations. In total, for 10 human subjects out of 15, a positive correlation between vision-based indicators, skin temperature, and thermal sensations were observed demonstrating promising potential in inferring thermal sensations of occupants with sufficient sensitivity 

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4. Investigation of the physiological differences in the immersive virtual reality environment and real indoor environment: Focused on skin temperature and thermal sensation

   Yeom, D. ; Choi, J. ( January 2019 , Building and environment)

   The goal of this research is to investigate and determine whether the effect of an IVE condition on an occupant's environmental sensations and physiological responses is different from the effect of a real environmental condition in the indoor environment. The research included a series of human subject experiments, with 16 participants in an environmental chamber. A thermal quality condition was selected as a primary environmental parameter, based on current IEQ-relevant studies. While the ambient thermal condition was gradually changed from 20 °C to 30 °C, the participants were asked to report their overall thermal sensations. Their skin temperatures were also continuously measured to collect physiological signal information in real time. The results of this experimental study revealed that the participants mostly generated higher skin temperature at the selected seven skin areas. Their reported thermal sensations were significantly higher in the IVE condition, than in the real environment, showing a difference of 12%. 

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5. Application of Causal Inference to the Analysis of Occupant Thermal State and Energy Behavioral Intentions in Immersive Virtual Environments

   https://doi.org/10.3389/frsc.2021.730474  

   Rentala, Girish ; Zhu, Yimin ; Mukhopadhyay, Supratik ( November 2021 , Frontiers in Sustainable Cities)

   Identification and quantitative understanding of factors that influence occupant energy behavior and thermal state during the design phase are critical in supporting effective energy-efficient design. To achieve this, immersive virtual environments (IVEs) have recently shown potential as a tool to simulate occupant energy behaviors and collect context-dependent behavior data for buildings under design. On the other hand, prior models of occupant energy behaviors and thermal states used correlation-based approaches, which failed to capture the underlying causal interactions between the influencing factors and hence were unable to uncover the true causing factors. Therefore, in this study, the authors investigate the applicability of causal inference for identifying the causing factors of occupant/participant energy behavioral intentions and their thermal states in IVE condition and compare those results with the baseline in-situ condition. The energy behavioral intentions here are a proximal antecedent of actual energy behaviors. A set of experiments involving 72 human subjects were performed through the use of a head-mounted device (HMD) in a climate chamber. The subjects were exposed to three different step temperatures (cool, neutral, warm) under an IVE and a baseline in-situ condition. Participants' individual factors, behavioral factors, skin temperatures, virtual experience factors, thermal states (sensation, acceptability, comfort), and energy behavioral intentions were collected during the experiments. Structural causal models were learnt from data using the elicitation method in conjunction with the PC-Stable algorithm. The findings show that the causal inference framework is a potentially effective method for identifying causing factors of thermal states and energy behavioral intentions as well as quantifying their causal effects. In addition, the study shows that in IVE experiments, the participants' virtual experience factors such as their immersion, presence, and cybersickness were not the causing factors of thermal states and energy behavioral intentions. Furthermore, the study suggests that participants' behavioral factors such as their attitudes toward energy conservation and perceived behavioral control to conserve energy were the causing factors of their energy behavioral intentions. Also, the indoor temperature was a causing factor of general thermal sensation and overall skin temperature. The paper also discusses other findings, including discrepancies, limitations of the study, and recommendations for future studies. 

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