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Today’s commercially-off-the-shelf (COST) wearable devices can unobtrusively capture several important parameters that may be used to measure the indoor comfort of building occupants, including ambient air temperature, relative humidity, skin temperature, perspiration rate, and heart rate. These data could be used not only for improving personal wellbeing, but for adjusting a better indoor environment condition. In this study, we have focused specifically on the sleeping phase. The main purpose of this work was to use the data from wearable devices and smart meters to improve the sleep quality of residents living at KTH Live-in-Lab. The wearable device we used was the OURA ring which specializes in sleep monitoring. In general, the data quality showed good potential for the modelling phase. For the modelling phase, we had to make some choices, such as the programming language and the AI algorithm, that was the best fit for our project. First, it aims to make personal physiological data related studies more transparent. Secondly, the tenants will have a better sleep quality in their everyday life if they have an accurate prediction of the sleeping scores and ability to adjust the built environment. Additionally, using knowledge about end users can help the building owners to design better building systems and services related to the end-user’s wellbeing.

Monitoring and Predicting Occupant’s Sleep Quality by Using Wearable Device OURA Ring and Smart Building Sensors Data (Living Laboratory Case Study)