Despite almost a century of research on thermal comfort, its provision is still based on fundamentally flawed assumptions, achieves a lackluster performance, and requires excessive energy to operate. Indeed, the leading thermal comfort model\backslashtextemdash the PMV model\backslashtextemdash is inaccurate because it was inferred from highly controlled experiments; thus, it is oblivious of important real-life factors that influence thermal comfort. The successor to the PMV model\backslashtextemdash the adaptation model\backslashtextemdash has assumptions that are at odds with social norms and business etiquette. Furthermore, the adaptation model has very permissive comfort zones to the extent that it may lead to thermal discomfort primary because winter cold waves, long hot summers and heatwaves push the adaptation beyond human’s physiological adaptations limits. Finally, while existing personalized approaches allow the users to manually set their thermal preferences, hand-operated control leads to rebound and overshoot. Consequently, it is often suggested that an optimum personalized thermal comfort approach needs to automatically estimate and provide thermal comfort with little or no user interaction. This thesis proposes a novel technique that provides an energy-efficient and personalized thermal comfort based on the fluctuations in heartbeat patterns. Indeed, humans have thermoregulation processes that maintain their core temperature at a specific constant. Thermoregulation involves massive changes in blood circulation. For instance, in hot conditions, blood circulation to the skin significantly increases in order to enhance heat dissipation through the skin. Conversely, in cold environments, blood circulation is restricted in order to reduce heat dissipation. As a result, this thesis hypothesizes that heart rate variability~(HRV) would be an accurate and genuine indicator of thermal comfort. The proposed approach has two main advantages. First, unlike existing personalized approaches\backslashtextemdash which are manually controlled or depend on predetermine settings\backslashtextemdash the proposed approach is self-adaptive. It uses the estimated thermal comfort and creates a duplex communication between the personal thermal comfort actuators (e.g., air conditioning units, chair warmers, and a neck cooler) and its users in order to provide a real-time thermal comfort that reflects each individual’s thermal expectations. Secondly, a few parts of the body (e.g., head, wrists, and feet) are mostly responsible for one’s overall thermal comfort. That being so, the proposed approach\backslashtextemdash unlike e.g., air conditioning units which cool or warm an entire room, including its walls and furniture, and regardless of the number of people present\backslashtextemdash would be energy-efficient because it would make it possible to create a microclimate comfort zone around a person and to channel the comfort only to the parts of the body that are most sensitive to thermal comfort. The research conducted in this thesis confirms these hypotheses. In summary, it was observed that indoor thermal conditions influence people’s heart rate variability and that it is possible to reliably (with an accuracy }\textgreater}95\backslash%) predict the subjects’ thermal comfort. These results led to the development of a prototype of a thermal comfort provision system that infers an individual’s thermal comfort from a photoplethysmogram (PPG) signal recorded on a wrist. Nevertheless, because thermal comfort is expressed differently from one person to another, the proposed approach would only work if person-specif machine learning models were developed for each user of the system. Such limitations would be costly for large scale development. Hence, an algorithm that mitigates this limitation was developed. The thesis also investigates the interplay between thermal comfort and stress and concludes with the suggestion that, although both thermal comfort and stress affect a person’s HRV, thermal comfort and stress have different etiologies and physiological responses. Ergo, their effect on a person’s HRV is perhaps non-overlapping and that the two can be distinguished by, e.g., a machine learning model. Finally, the thesis coins the concept of \backslashenquote{affect-aware thermal comfort} as a complementary to the existing thermal comfort provision methods. An affect-aware system would estimate in real-time the affects (e.g., thermal comfort, stress, and emotion) of its users and automatically adjusts itself in order to satisfy their implicit and explicit needs (in terms of, e.g., thermal comfort, ventilation, well-being, and productivity) and in a sustainable way (in terms of e.g., heating, ventilation and cooling efficiency, and lighting).
