WiSleep: Smartphone-driven Sleep Population Monitoring with Unsupervised Learning

With sleep deprivation being a public health concern, sleep-monitoring technology, mainly through consumer-grade wearables, has shown value among users to better understand their most fundamental measure of health. Unfortunately, utilizing wearable technology is bound to the conditions of users owning these devices and using them to bed every night. While wearables can deliver highly personalized sleep insights to users, they inadvertently affect the ability of sleep monitoring solutions to reach unprivileged sections of society due to added costs and device accessibility. With our primary motivation to promote sleep monitoring for public health use cases at the population scale, we developed WiSleep, a sleep monitoring system that infers sleep duration from solely relying on a user’s smartphone without requiring a wearable device. Unlike prior efforts that use supervised learning methods and require labelled training data to train sleep models, our method is based on unsupervised learning, which enables easy deployment to new population groups or new regions without a need for labelled data collection and training. Specifically, we employ the smartphone activity of the user, represented by time series of WiFi network event rates, as input data to infer the user’s sleep duration (i.e., sleep time and wake time) through an unsupervised Bayesian change-point detection ensemble model. Our evaluation shows WiSleep’s efficacy in being a low-cost accessible sleep monitoring approach. We present results that yield comparable performance to prior techniques, particularly those requiring new users’ labeled data to achieve model personalization. System evaluation from a user study achieved an average of 93.68% accuracy within 59 minutes sleep time error, 31 minutes wake time error, and 57 minutes sleep duration error by utilizing coarse-grained time series data. We demonstrate the application of our technique to predict sleep for 1000 anonymous users and enable population-scale analytics with low computational overhead.