Cardio-metabolic risk modeling and assessment through sensor-based measurements.

Abstract

Cardio-metabolic risk assessment in the general population is of paramount importance to reduce diseases burdened by high morbility and mortality. The present paper defines a strategy for out-of-hospital cardio-metabolic risk assessment, based on data acquired from contact-less sensors. We employ Structural Equation Modeling to identify latent clinical variables of cardio-metabolic risk, related to anthropometric, glycolipidic and vascular function factors. Then, we define a set of sensor-based measurements that correlate with the clinical latent variables. Our measurements identify subjects with one or more risk factors in a population of 68 healthy volunteers from the EU-funded SEMEOTICONS project with accuracy 82.4%, sensitivity 82.5%, and specificity 82.1%. Our preliminary results strengthen the role of self-monitoring systems for cardio-metabolic risk prevention.