Multiarmed Bandits for Sleep Recognition of Elderly Living in Single-Resident Smart Homes

Sleep is an essential activity that affects an individual’s health and ability to perform activities of daily living (ADL). Inadequate sleep reduces cognitive capacity and leads to health-related issues, such as cardiovascular diseases. Sleep disorders are more prevalent in older adults. Therefore, it is essential to recognize sleep patterns and support older adults and their caregivers. In our study, we collect data in real-world unconstrained and nonintrusive environments. This article presents a novel sleep activity recognition method using motion sensors for recognizing nighttime and daytime sleep, which can further enable the development of insightful healthcare applications. The research objectives are to evaluate the application of using multiarmed bandit (MAB) methods to 1) learn normal sleep patterns; 2) evaluate sleep quality; and 3) detect anomalies in sleep activity for 11 elderly participants living in single-resident smart homes. We evaluate the performance of Thompson sampling (TS), random selection, and upper confidence bound MAB methods. TS outperformed the other two methods. Our findings show most elderly participants slept between 6 and 8 h with 85% sleep efficiency and up to three awakenings per night.