Due to a globally aging population, there is a growing demand for smart home technology which can serve to monitor the health and safety of adults. Therefore, sleep monitoring has emerged as a crucial tool to improve the health and autonomy of adults. While polysomnography (PSG) is an effective and accurate tool for sleep monitoring, it is obtrusive as the user must wear the instruments during the experiment. Therefore, there has been a growing interest in deploying unobtrusive sleep monitoring devices, specifically for long-term patient monitoring. This thesis proposes multiple algorithms applicable to unobtrusive pressure sensitive sensor arrays in order to assess sleep quality. These algorithms can be listed as adaptive movement detection, sensor data fusion and bed occupancy detection. This thesis also investigates long-term sleep pattern changes from previously recorded data. The methods developed in the thesis can be of interest for future clinical remote patient monitoring systems.