As the economy progresses and new technologies emerge, more people are struggling with sleep-related difficulties. Poor sleep quality adversely affects people's health and well-being, productivity, academic success, and cognitive capability. These impairments can also affect traffic and industrial safety, and national economic developments. To better tackle these problems, it is important to accurately understand people's sleep quality. In this work, we present approaches to accurately estimate a user's sleep duration, which will facilitate better estimation of sleep quality. We apply generalized linear model (GLM) and generalized linear mixed model (GLMM), which takes person variability into consideration in addition to fixed effects, such as various temporal factors (sleep start time, days of a week, etc.), weather, a user's daily activities and calendar entries to estimate sleep duration. Through our analysis of a longitudinal sensor dataset collected from the smartphones and Fitbits of a cohort of 18 on-campus college students over an extended period of time, we show the feasibility of the work with correlations of up to 0.745 between the pairs of actual and estimated sleep durations.
