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

TL;DR

This paper presents a novel method for passive sleep activity recognition using smartphone data, employing a Heterogeneous Hidden Markov Model to monitor sleep patterns objectively and non-invasively.

Contribution

It introduces a general, self-supervised approach that leverages ubiquitous smartphone sensors to assess sleep without relying on traditional, intrusive methods.

Findings

Effective sleep characterization validated against wearable data

Continuous, non-invasive sleep monitoring possible with smartphones

Heterogeneous Hidden Markov Model outperforms baseline methods

Abstract

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.