Smart Home: Learning of Routines

This thesis focuses on the design, development, and implementation of an innovative smart system that uses user sleep data and machine learning for intelligent control of lights in a household. The main goal is to improve the user’s sleep quality through the optimization of light exposure, particularly by reducing exposure to cool daylight before sleep. The work is characterized by an emphasis on the use of readily available data, cost-effective hardware devices, and open-source technologies. The primary source of data was the FitBit Charge 5 smartwatch, which provides detailed information about the user’s sleep patterns. The created machine learning model, specifically linear regression, is hosted on the Raspberry Pi platform, which also serves as the central unit for controlling lights and managing the overall system. In addition to light control, the system also supports sending notifications to the user about the approaching recommended sleep start time contributing to the overall improvement of sleep hygiene and sleep quality