Wearable-oriented Support for Interpretation of Behavioural Effects on Sleep.

Daily behaviour directly impacts health in the short and long term. Thus, embracing and maintaining healthy behaviours work like a preventive action, avoiding or delaying the emergence of chronic diseases. The process of changing daily routines toward healthy behaviours starts by understanding the current problems. Wearable and deep learning (DL) technologies represent important resources for supporting such an understanding. This paper discusses a strategy to interpret multifeatured longitudinal wearable data to analyse possible causes of health issues. We use the sleep domain as a case example where the aim is to clarify the reasons for poor sleep quality. A dataset with wearable data of 1874 days was used to create an explainable DL model, which indicates the main day-before-night sleep behaviours that may cause poor sleep quality. We use a comparative analysis with a hormone-based framework for sleep control as the form of validation. The results show that the explanations corroborate the results of the literature. However, other datasets with more features should be explored to verify the combination of these features and their effects on the health aspect under study.