2024/05/06 by Christopher Thornton, Thornton, Christopher, Billy C. Smith +7
Computer Science · #FOS: Biological sciences #Neural Networks and Applications #Neurons and Cognition (q-bio.NC)
paper · pdf · doi:10.48550/arxiv.2405.03829
openalex publication_date 2024/05/06 · openalex created_date 2024/05/11 · openalex updated_date 2026/07/28
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.