Wize Mirror - a smart, multisensory cardio-metabolic risk monitoring system

In the recent years personal health monitoring systems have been gaining popularity, both as a result of the pull from the general population, keen to improve well-being and early detection of possibly serious health conditions and the push from the industry eager to translate the current significant progress in computer vision and machine learning into commercial products. One of such systems is the Wize Mirror, built as a result of the FP7 funded SEMEOTICONS (SEMEiotic Oriented Technology for Individuals CardiOmetabolic risk self-assessmeNt and Self-monitoring) project. The project aims to translate the semeiotic code of the human face into computational descriptors and measures, automatically extracted from videos, multispectral images, and 3D scans of the face. The multisensory platform, being developed as the result of that project, in the form of a smart mirror, looks for signs related to cardio-metabolic risks. The goal is to enable users to self-monitor their well-being status over time and improve their life-style via tailored user guidance. This paper is focused on the description of the part of that system, utilising computer vision and machine learning techniques to perform 3D morphological analysis of the face and recognition of psycho-somatic status both linked with cardio-metabolic risks. The paper describes the concepts, methods and the developed implementations as well as reports on the results obtained on both real and synthetic datasets

Beyond clustering : rethinking the segmentation of energy consumers when nudging them towards energy-saving behavior

Besides technological innovations in energy production and management technologies, the fight against climate change requires fundamental changes in our energy consumption behavior. Behavioral interventions are key to this process, especially when tailored to different energy consumer segments accounting for their socio-demographic profiles, socio- psychological characteristics and energy consumption practices. In this work, we propose a novel approach to energy consumer segmentation that facilitates the choice of (nudging) interventions for each segment. We call it intervention-driven energy consumer profiling since it explicitly considers upfront the set of interventions that can be delivered to energy consumers and defines profiles that can be readily matched with them. The profiles are specified as combinations of socio-psychological factors with implications for energy-saving behavior and are parameterized by thresholds that measure how strongly these factors are represented in each profile. One profile represents ideal energy-savers, whereas each of the remaining five profiles shares one or two distinct features that serve as barriers towards energy-saving behavior and/or prescribe specific type of nudging interventions for strengthening such behavior. We use the responses of users to a European-wide online survey to formulate and solve an optimization problem for these thresholds and then assign the survey respondents to the profiles. Finally, we analyze them also in terms of socio-demographic variables and recommend appropriate nudging interventions for them