Ensemble | Modelling glucose levels from wearable biosensors

Talk abstracts

Probabilistic modelling of glucose levels using temporal data from multiple wearable biosensors

A new generation of wearable biosensors and mobile applications can now measure physiological variables with high time resolution and can provide essential information about the health status of an individual. Here I will present our ongoing study that aims to characterise relationships between physiological systems by using multiple wearable devices simultaneously. In our study we measure meals, glucose levels, heart rate and physical activity, and the ultimate goal is to create a mathematical model that explains the relationships between all of the variables on an individual, personalised basis.

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Speaker bio

Nick studied physics at Durham University before obtaining his PhD in Systems Biology at the University of Manchester. Nick then started a postdoc with Prof Felix Naef at the EPFL (Switzerland) in 2016, where the primary research focus was in the use of mathematical models and Bayesian data analysis to understand stochastic single-cell gene expression. Nick is currently transitioning into personalised health, and he received a Transition Postdoc Fellowship from Personalized Health and Related Technologies (PHRT, Switzerland). He is currently conducting a study on humans using multiple wearable biosensors, and he has recently moved to Geneva University Hospitals (HUG) to finish the project.