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

arXiv: https://arxiv.org/abs/2211.10371 · HTML: https://mlsanity.com/paper/2211.10371/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 25 references that are in the map.
- First posted on arXiv 8 Nov 2022.

## Builds on (most cited first, 1 of 1)

- [PyHHMM: A Python Library for Heterogeneous Hidden Markov Models](https://mlsanity.com/paper/2201.06968/) (2022)

## Built on by (most cited first, 2 of 2)

- [Rough Transformers for Continuous and Efficient Time-Series Modelling](https://mlsanity.com/paper/2403.10288/) (2024)
- [Sleep Activity Recognition and Characterization from Multi-Source Passively Sensed Data](https://mlsanity.com/paper/2301.10156/) (2023)

## Abstract (arXiv)

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.