Heterogeneous Hidden Markov Models for Sleep Activity Recognition from Multi-Source Passively Sensed Data
This work addresses sleep activity recognition for psychiatric patient monitoring, but it is incremental as it applies an existing method to new data.
The authors tackled the problem of identifying major sleep episodes from passively sensed smartphone data to monitor psychiatric patients, proposing a Heterogeneous Hidden Markov Model that was validated against clinically tested wearables.
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.