  Sleep is the primary mean of recovery from accumulated fatigue and thus plays
a crucial role in fostering people's mental and physical well-being. Sleep
quality monitoring systems are often implemented using wearables that leverage
their sensing capabilities to provide sleep behaviour insights and
recommendations to users. Building models to estimate sleep quality from sensor
data is a challenging task, due to the variability of both physiological data,
perception of sleep quality, and the daily routine across users. This challenge
gauges the need for a comprehensive dataset that includes information about the
daily behaviour of users, physiological signals as well as the perceived sleep
quality. In this paper, we try to narrow this gap by proposing Bilateral Heart
rate from multiple devices and body positions for Sleep measurement (BiHeartS)
dataset. The dataset is collected in the wild from 10 participants for 30
consecutive nights. Both research-grade and commercial wearable devices are
included in the data collection campaign. Also, comprehensive self-reports are
collected about the sleep quality and the daily routine.

Human-Computer Interaction