Heterogeneous Hidden Markov Models for Sleep Activity Recognition from Multi-Source Passively Sensed Data

TL;DR

This paper introduces a Heterogeneous Hidden Markov Model to identify sleep episodes from passively sensed smartphone data, aiding psychiatric monitoring by detecting behavioral changes linked to sleep disturbances.

Contribution

It proposes a novel Heterogeneous Hidden Markov Model for sleep activity recognition using multi-source passive data, validated against clinical wearable metrics.

Findings

Effective sleep episode detection from passively sensed data

Validation shows high accuracy against clinical wearables

Supports real-time behavioral monitoring in psychiatric care

Abstract

Psychiatric patients' passive activity monitoring is crucial to detect behavioural shifts in real-time, comprising a tool that helps clinicians supervise patients' evolution over time and enhance the associated treatments' outcomes. Frequently, sleep disturbances and mental health deterioration are closely related, as mental health condition worsening regularly entails shifts in the patients' circadian rhythms. Therefore, Sleep Activity Recognition constitutes a behavioural marker to portray patients' activity cycles and to detect behavioural changes among them. Moreover, mobile passively sensed data captured from smartphones, thanks to these devices' ubiquity, constitute an excellent alternative to profile patients' biorhythm. In this work, we aim to identify major sleep episodes based on passively sensed data. To do so, a Heterogeneous Hidden Markov Model is proposed 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 clinically tested wearables, proving the effectiveness of the proposed approach.