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Thermal comfort is a significant factor in the indoor building environment because it influences both human productivity and health. A currently popular method for predicting thermal comfort levels, the Predicted Mean Vote (PMV) and Predicted Percent Dissatisfied (PPD) model, unfortunately, has certain limitations. Consequently, the development of a better method for making accurate predictions (especially for individuals) is needed. Our goal was to develop a tool to predict individual thermal comfort preferences and automatically control the heating, ventilation, and air conditioning (HVAC) systems. This study adopted a series of human-subject experiments to collect essential data. All collected data was analyzed by adopting different machine learning algorithms. The machine learning algorithms predicted individual thermal comfort levels and thermal sensations, based on facial skin temperatures of participants in the experiments. These predictions were input data for the HVAC system control model, and results supported the potential for using facial skin temperatures to predict thermal comfort and thermal sensation levels. Moreover, this tool provided automatic control of the HVAC systems that can help improve the indoor environment of a building. 

Lighting, as a significant component of indoor environment quality, was found to be a primary contributor to deficient indoor environments in today’s workplace. This resulted from the fact that current guidelines are derived from empirical values and neglect the prevalence of computer-based tasks in current offices. A personal visual comfort model was designed to predict the degree of an individual’s visual comfort, as a way of evaluating the indoor lighting of the environment. Development of the model relied on experimental data, including individual eye pupil sizes, visual sensations, and visual satisfaction in response to various illuminance levels used for tests of six human subjects. The results showed that (1) A personal comfort model was needed, (2) the personal comfort model produced a median accuracy of 0.7086 for visual sensation and 0.65467 for visual satisfaction for all subjects; (3) To develop a prediction model for the sample group, the Support Vector Machine algorithm,, outperformed the Logistic Regression and the Gaussian Naïve Bayes in terms of prediction accuracy. It was concluded that, a personal visual comfort model can use a building’s occupant’s eye pupil size to generate an accurate prediction of that occupant’s visual sensations and visual satisfaction that can, therefore, be applied with lighting control to improve the indoor environment and energy use in that building. 

Heating, ventilation and air-conditioning (HVAC) systems have been adopted to create comfortable, healthy and safe indoor environments. In the control loop, the technical feature of the human demand-oriented supply can help operate HVAC effectively. Among many technical options, real time monitoring based on feedback signals from end users has been frequently reported as a critical technology to confirm optimizing building performance. Recent studies have incorporated human thermal physiology signals and thermal comfort/discomfort status as real-time feedback signals. A series of human subject experiments used to be conducted by primarily adopting subjective questionnaire surveys in a lab-setting study, which is limited in the application for reality. With the help of advanced technologies, physiological signals have been detected, measured and processed by using multiple technical formats, such as wearable sensors. Nevertheless, they mostly require physical contacts with the skin surface in spite of the small physical dimension and compatibility with other wearable accessories, such as goggles, and intelligent bracelets. Most recently, a low cost small infrared camera has been adopted for monitoring human facial images, which could detect the facial skin temperature and blood perfusion in a contactless way. Also, according to latest pilot studies, a conventional digital camera can generate infrared images with the help of new methods, such as the Euler video magnification technology. Human thermal comfort/discomfort poses can also be detected by video methods without contacting human bodies and be analyzed by the skeleton keypoints model. In this review, new sensing technologies were summarized, their cons and pros were discussed, and extended applications for the demand-oriented ventilation were also reviewed as potential development and applications. 

Lighting is the most crucial factor impacting an occupants’ visual comfort in a building environment. However, most prevailing current lighting guidelines deriving from empirical values are designed primarily for paper-based tasks, rather than computer-based. In many cases, present guidelines have been reported that there is a limitation to meet the needs for a user’s new task types. Above all, existing technical tools also have a limited function to evaluate a user’s real-time visual perception which can be applied as an indicator to control a building lighting system. This research estimated each individual participant's visual sensations by analyzing pupil sizes and their change patterns since the human body have the physiological regulation ability which naturally minimizes the adverse effects of the surrounding environment on the human body. This study adopted a series of human subject experiments which were performed in an environmental chamber of USC. Based on a computer-based task which are most commonly performed in current offices, various ranges of ambient lighting parameters, including luminance (cd/m2), illuminance (lux), contrast ratio, and UGR, were generated and controlled while each subject’s pupil sizes were recorded. The experimental result data were statistically analyzed to identify a relationship between human visual sensations, lighting parameters, and also pupil sizes by ethnic origin and myopia condition. The research outcomes showed the potential use of pupil sizes for estimating an individual’s visual sensation, and confirmed the principle as an applicable technology to integrate an environmental design and control system with the help of a real-time sensing device.