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Sleep constitutes a key indicator of human health, performance, and qualityof life. Sleep deprivation has long been related to the onset, development, andworsening of several mental and metabolic disorders, constituting an essentialmarker 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. Inthis work, we propose a general method that continuously operates on passivelysensed data from smartphones to characterize sleep and identify significantsleep episodes. Thanks to their ubiquity, these devices constitute an excellentalternative data source to profile subjects' biorhythms in a continuous,objective, and non-invasive manner, in contrast to traditional sleep assessmentmethods that usually rely on intrusive and subjective procedures. AHeterogeneous Hidden Markov Model is used to model a discrete latent variableprocess associated with the Sleep Activity Recognition task in aself-supervised way. We validate our results against sleep metrics reported bytested wearables, proving the effectiveness of the proposed approach andadvocating its use to assess sleep without more reliable sources.

Sleep Activity Recognition and Characterization from Multi-Source Passively Sensed Data