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People spend most of their day in buildings, and a large portion of the energy in buildings is used to control the indoor environment for creating acceptable conditions for occupants. However, the majority of the building systems are controlled based on a "one size fits all" scheme which cannot account for individual occupant preferences. This leads to discomfort, low satisfaction and negative impacts on occupants' productivity, health and well-being. In this paper, we describe our vision of how recent advances in Internet of Things (IoT) and machine learning can be used to add intelligence to an office desk to personalize the environment around the user. The smart desk can learn individual user preferences for the indoor environment, personalize the environment based on user preferences and act as an intelligent support system for improving user comfort, health and productivity. We briefly describe the recent advances made in different domains that can be leveraged to enhance occupant experience in buildings and describe the overall framework for the smart desk. We conclude the paper with a discussion of possible avenues for further research.

Despite the large share of energy consumption, current HVAC systems in buildings fail to meet their primary purpose of maintaining comfortable indoor conditions. Current "one size fits all" approach to control the thermal conditions in an environment lead to a high degree of occupant dissatisfaction. Advancements in Internet of Things and Machine Learning have opened the possibility of deploying different sensors at a wide scale to monitor environmental and physiological information and using collected sensor data to model individual comfort requirements. Thermal imaging has recently gained interest as one of the possible ways to monitor physiological information (skin temperature) for thermal comfort assessment. Previous studies have shown that skin temperatures from different regions of the face, such as forehead, nose, cheeks and ears can provide useful information for predicting thermal sensation at an individual level. However, existing approaches to process thermal images either rely on manual temperature extraction or use methods that are less reliable in accurately identifying different facial regions. One of the major challenges of using thermal imaging for monitoring skin temperatures in actual buildings is that occupants may move relative to the camera. It is not practical to expect building occupants to be oriented facing the cameras at all times, therefore, it is important to be able to extract as much information as possible from instances where it is feasible to extract relevant information. In this paper, we describe an approach to extract skin temperature by locating specific regions of the face in thermal images. The approach involves combining data from RGB images with thermal images and leveraging facial landmark detection in RGB images. We also evaluate our approach with existing approach of face detection used in previous studies. Our study demonstrates that facial landmark detection provides a more accurate calculation of different locations in the face compared to previous studies. We show an improvement in overall quantity and quality of temperature measurements extracted from thermal images compared to previous studies. More accurate temperature measurements from thermal images can improve the accuracy of thermal imaging for modeling and predicting thermal comfort.

as-damaged' point cloud data and 'as-built' models. Yet research efforts to develop and rigorously test appropriate methods are seriously hampered by the obvious scarcity of access for researchers to earthquake-damaged buildings for surveying specimens and hence the lack of terrestrial laser scanning data of post-earthquake buildings. Full-or reduced-scale physical models of building components can be built and damaged using a shaking table or other structural laboratory equipment, and these can be scanned, all at reasonable cost. However, equivalent full-scale building samples are unavailable. The solution is to synthesize accurate and representative data sets. A computational approach for compiling such data sets, including BIM modeling of damaged buildings and synthetic scan generation, is proposed. The approach was validated experimentally through compilation of two full-scale models of buildings damaged in earthquakes in Turkey.