SensibleSleep: A Bayesian Model for Learning Sleep Patterns from Smartphone Events

PLoS One. 2017 Jan 11;12(1):e0169901. doi: 10.1371/journal.pone.0169901. eCollection 2017.

Abstract

We propose a Bayesian model for extracting sleep patterns from smartphone events. Our method is able to identify individuals' daily sleep periods and their evolution over time, and provides an estimation of the probability of sleep and wake transitions. The model is fitted to more than 400 participants from two different datasets, and we verify the results against ground truth from dedicated armband sleep trackers. We show that the model is able to produce reliable sleep estimates with an accuracy of 0.89, both at the individual and at the collective level. Moreover the Bayesian model is able to quantify uncertainty and encode prior knowledge about sleep patterns. Compared with existing smartphone-based systems, our method requires only screen on/off events, and is therefore much less intrusive in terms of privacy and more battery-efficient.

MeSH terms

  • Bayes Theorem
  • Biobehavioral Sciences
  • Habits
  • Humans
  • Learning / physiology*
  • Models, Biological
  • Probability
  • Sleep / physiology*
  • Smartphone*

Grants and funding

AC is funded in part by the High Resolution Networks project (The Villum Foundation), as well as Social Fabric (University of Copenhagen). PB is supported in part by the Innovation Fund Denmark through the project Eye Tracking for Mobile Devices. Sony Mobile provided support in the form of salaries for authors VS and HJ, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.