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Project reference: 611516
Funded under: FP7-ICT

SEMEiotic Oriented Technology for Individual's CardiOmetabolic risk self-assessmeNt and Self-monitoring

From 2013-11-01 duration 36 months

Project Website

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Atherosclerotic cardiovascular diseases (CVDs), including heart disease and stroke, are the leading causes of mortality worldwide. The atherosclerotic illness develops insidiously, and clinical manifestations often become evident in its advanced stages.  This explains why CVDs represent one of the major challenges to the health systems and considerable efforts are profused to treat clinical manifestations of CVDs. These efforts have granted significant advances with actual improvements in patients’ outcome, quod ad vitam and valitudinem.

Despite the success of the pharmacological, interventional, and surgical treatment of the CVDs,  all these therapies cannot modify the epidemiological impact of the disease. Moreover, the cost of current health systems grows exponentially with the widespread use of complex diagnostic procedures, as well as with population aging. At present, the strategy of prevention, which attempts to modify some patho-physiological factors related to the genesis of the disease, seems to be the only way to limit the epidemic growth of CVDs.

Cardio-metabolic risk is a cluster of risk factors indicative of a patient’s overall risk for CVD and type-2 diabetes. They include obesity, physical inactivity, smoke, alcohol abuse, abnormal lipid metabolism, hyper-glycaemia, and arterial hypertension. Educational programs and lifestyle interventions represent effective tools for reducing cardio-metabolic risk profile and incidence of CVDs.  Such a prevention strategy is individually tuned and requires an expensive organization of the health systems. A rationale alternative to  intensive individual coaching is the development of systems for self-learning and self-monitoring. These systems may help people to change and maintain their lifestyle providing tailored suggestions about nutrition, weight, physical activity, fatigue, and stress according to daily surveys.  Data collected by such coaching systems could be analysed and interpreted by health care professionals so as to support decision making targeted to the specific individual conditions. This approach has the potential to result highly cost-effective and might foster the diffusion of self-coaching systems with favourable impact on social, physiological, and environmental factors that, at present, remain barriers for the success of large-scale preventive intervention on CVD and diabetes.

In SEMEOTICONS, we propose the definition of the digital semeiotics of the face, i.e. the computerized evaluation of facial signs, focused on those signs that relate to some widely-recognized risk factors of CVDs. These signs above will assessed by a number of computational descriptors that will be extracted from different observation modalities (morphometric, biometric, colorimetric, gestural and emotional analyses of the face, spectroscopic analysis of skin and iris, sub-cutaneous substances and the function of sub-cutaneous tissues, compositional analysis of breath and exhaled gas). An interactive smart mirror, easily deployable in normal-life settings, will be developped which will seamlessly integrate contactless sensors, such as three-dimensional (3D) optical sensors, a multispectral camera, gas detection sensors, and microphones. A touch-screen interface will be also included for user’s interactions and output visualization. The resulting device will be a kind of “wise wizard” mirror, called Wize Mirror. The integration of semeiotic descriptors will occur by a virtual individual’s model used to compute and trace the daily evolution of an individual’s wellness index.  A health diary about this index will be created so as to enable the individual to evaluate and personally relate his/her lifestyle to his/her well-being. A personalized user’s guidance will be supplied according to user’s profiling so as to provide useful suggestion on correct lifestyle self-monitoring.