Unsupervised Machine Learning Identifies Latent Ultradian States in Multi-Modal Wearable Sensor Signals

TL;DR

This study employs an unsupervised hidden semi-Markov model to identify 26 latent ultradian states from multi-modal wearable sensor data, revealing their temporal patterns and associations with mood and daily activities.

Contribution

It introduces a novel unsupervised approach using hidden semi-Markov models to detect latent physiological states from multi-modal wearable data.

Findings

Identified 26 distinct ultradian states across participants.

Many states showed specific times of day occurrence.

Some states correlated with subjective mood and daily activities.

Abstract

Wearable sensors such as smartwatches have become ubiquitous in recent years, allowing the easy and continual measurement of physiological parameters such as heart rate, physical activity, body temperature, and blood glucose in an every-day setting. This multi-modal data offers the potential to identify latent states occurring across physiological measures, which may represent important bio-behavioural states that could not be observed in any single measure. Here we present an approach, utilising a hidden semi-Markov model, to identify such states in data collected using a smartwatch, electrocardiogram, and blood glucose monitor, over two weeks from a sample of 9 participants. We found 26 latent ultradian states across the sample, with many occurring at particular times of day. Here we describe some of these, as well as their association with subjective mood and time use diaries. These methods provide a novel avenue for developing insights into the physiology of everyday life.