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

Andrea Cuttone, Per Bækgaard, Vedran Sekara, Håkan Jonsson, Jakob Eg Larsen, Sune Lehmann Jørgensen

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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.
Original languageEnglish
Article numbere0169901
JournalP L o S One
Volume12
Issue number1
Pages (from-to)1-20
ISSN1932-6203
DOIs
Publication statusPublished - 2017

Bibliographical note

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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