  Sleep constitutes a key indicator of human health, performance, and quality
of life. Sleep deprivation has long been related to the onset, development, and
worsening of several mental and metabolic disorders, constituting an essential
marker for preventing, evaluating, and treating different health conditions.
Sleep Activity Recognition methods can provide indicators to assess, monitor,
and characterize subjects' sleep-wake cycles and detect behavioral changes. In
this work, we propose a general method that continuously operates on passively
sensed data from smartphones to characterize sleep and identify significant
sleep episodes. Thanks to their ubiquity, these devices constitute an excellent
alternative data source to profile subjects' biorhythms in a continuous,
objective, and non-invasive manner, in contrast to traditional sleep assessment
methods that usually rely on intrusive and subjective procedures. A
Heterogeneous Hidden Markov Model is used to model a discrete latent variable
process associated with the Sleep Activity Recognition task in a
self-supervised way. We validate our results against sleep metrics reported by
tested wearables, proving the effectiveness of the proposed approach and
advocating its use to assess sleep without more reliable sources.
