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

TL;DR

SensibleSleep introduces a Bayesian approach to accurately infer sleep patterns from minimal smartphone event data, offering reliable estimates while respecting user privacy and reducing battery consumption.

Contribution

It presents a novel Bayesian model that accurately detects sleep patterns using only screen on/off events, improving privacy and efficiency over existing methods.

Findings

Achieves 0.89 accuracy in sleep detection

Reliable estimates at individual and group levels

Quantifies uncertainty in sleep pattern inference

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.