[2211.10371] Heterogeneous Hidden Markov Models for Sleep Activity Recognition from Multi-Source Passively Sensed Data
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Electrical Engineering and Systems Science > Signal Processing

arXiv:2211.10371 (eess)
[Submitted on 8 Nov 2022]

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

Authors:Fernando Moreno-Pino, María Martínez-García, Pablo M. Olmos, Antonio Artés-Rodríguez
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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.
Comments: Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2022, November 28th, 2022, New Orleans, United States & Virtual, this http URL, 10 pages (6 pages + 4 pages of references and appendices)
Subjects: Signal Processing (eess.SP); Machine Learning (cs.LG)
Cite as: arXiv:2211.10371 [eess.SP]
  (or arXiv:2211.10371v1 [eess.SP] for this version)
  https://doi.org/10.48550/arXiv.2211.10371

Submission history

From: Fernando Moreno-Pino [view email]
[v1] Tue, 8 Nov 2022 17:29:40 UTC (1,045 KB)
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