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

Abstract

Objective: 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.

Methods: 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.

Results: 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%.

Conclusions: Our preliminary results strengthen the role of self-monitoring systems for cardio-metabolic risk prevention.

Keywords: Cardio-metabolic risk; Risk modeling; Self Organizing Maps; Self-monitoring; Sensor-based measurements; Smart mirror; Structural Equation Modeling.