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.

It is recognized that stress conditions play an important role in the definition of individual wellness and represent a major risk factor for most non-communicable diseases. Most studies focus on the evaluation of response to maximal stress conditions while a few of them reports results about the detection/monitoring of response to mild stimulations. In this study, we investigate the capability of some physiological signs and indicators (including Heart Rate, Heart Rate Variability, Respiratory Rate, Galvanic Skin Response) to recognize stress in response to moderate cognitive activation in daily life settings. To achieve this goal, we built up an unobtrusive platform to collect signals from healthy volunteers (10 subjects) undergoing cognitive activation via Stroop Color Word Test. We integrated our dataset with data from the Stress Recognition in the Automobile Drivers dataset. Following data harmonization, signal recordings in both datasets were split into five-minute blocks and a set of 12 features was extracted from each block. A feature selection was implemented by two complementary approaches: Sequential Forward Feature Selection (SFFS) and Auto-Encoder (AE) neural networks. Finally, we explored the use of Self-Organizing Map (SOM) to provide a flexible representation of an individual status. From the initial feature set we have determined, by SFFS analysis, that 2 of them (median Respiratory Rate and number peaks in Galvanic Skin Response signals) can discriminate activation statuses from resting ones. In addition, AE experiments also support that two features can suffice for recognition. Finally, we showed that SOM can provide a comprehensive but compact description of activation statuses allowing a fine prototypical representation of individual status.

This study is part of a research project aiming to build a model for quantifying an individual wellness status through unobtrusive measurements of psychophysical parameters and self-reported data. In particular, we focus on the evaluation of the individual response to mild stress stimuli. The experimental setup included: EG05000 Medlab Five Channel Module, Gigabit Ethernet camera, BioHarness 3 Zephyr chest belt, and Muse 2 Headband. Experimental results show increased heart rate and respiration rate, and changes of the brain activity in the stress condition. This is consistent with a “fight or flight” response in accordance with literature. Therefore, the methodology applied in this study can be used to monitor the individual wellness status in conditions of mild stress stimuli.

OBJECTIVE:Endothelial function is important for regulating peripheral blood flow to meet varying metabolic demands and can be measured indirectly during vascular provocations. In this study, we compared the PAT finger response (EndoPAT) after a 5-minutes arterial occlusion to that from forearm skin comprehensive microcirculation analysis (EPOS).METHODS:Measurements in 16 subjects with varying cardiovascular risk factors were carried out concurrently with both methods during arterial occlusion, while forearm skin was also evaluated during local heating.RESULTS:Peak values for EPOS skin Perfconv and speed-resolved total perfusion after the release of the occlusion were significantly correlated to the EndoPAT RHI (ρ = .68, P = .007 and ρ = .60, P = .025, respectively), mainly due to high-speed blood flow. During local heating, EPOS skin oxygen saturation, SO2, was significantly correlated to RHI (ρ = .62, P = .043). This indicates that SO2 may have diagnostic value regarding endothelial function.CONCLUSIONS:We have demonstrated for the first time a significant relationship between forearm skin microcirculatory perfusion and oxygen saturation and finger PAT. Both local heating and reactive hyperemia are useful skin provocations. Further studies are needed to understand the precise regulation mechanisms of blood flow and oxygenation during these tests.

The Ambient Assisted Living (AAL) paradigm proposes advanced technologies and services to improve the quality of life, health, and wellbeing of citizens by making their daily-life activities easier and more secure, by monitoring patients under specific treatment, and by addressing at-risk subjects with proper counseling. The challenges brought by AAL range from robust, accurate, and nonintrusive data acquisition in daily-life settings to the development of services that are easy to use and appealing to the users and that support long-term engagement. This chapter offers a brief survey of existing vision-based monitoring solutions for personalized healthcare and wellness, and introduces the Wize Mirror, a multisensory platform featuring advanced algorithms for cardiometabolic risk prevention and quality-of-life improvement.

The Ambient Assisted Living (AAL) paradigm proposes advanced technologies and services to improve the quality of life, health, and wellbeing of citizens by making their daily-life activities easier and more secure, by monitoring patients under specific treatment, and by addressing at-risk subjects with proper counseling. The challenges brought by AAL range from robust, accurate, and nonintrusive data acquisition in daily-life settings to the development of services that are easy to use and appealing to the users and that support long-term engagement. This chapter offers a brief survey of existing vision-based monitoring solutions for personalized healthcare and wellness, and introduces the Wize Mirror, a multisensory platform featuring advanced algorithms for cardiometabolic risk prevention and quality-of-life improvement.

Unobtrusive monitoring of vital signs is relevant for both medical (patient monitoring) and non-medical applications (e.g., stress and fatigue monitoring). In this paper, we focus on the use of imaging photoplethysmography (iPPG). High frame rate videos were acquired by using a monochrome camera and an optical band-pass filter ([Formula: see text] nm). To enhance iPPG signal, we investigated the use of independent component analysis (ICA) pre-processing applied to iPPG signal from different regions of the face. Methodology was tested on [Formula: see text] healthy volunteers. Heart rate (HR) and standard time and frequency domain descriptors of heart rate variability (HRV), simultaneously extracted from videos and ECG data, were compared. A mean absolute error (MAE) about 3.812 ms was observed for normal-to-normal intervals with or without ICA pre-processing. Smaller MAE values of frequency domain descriptors were observed when ICA pre-processing was used. The impact of both video frame rate and video signal interval were also analyzed. All the results support the conclusion that proposed ICA pre-processing can effectively improve the HR and HRV assessment from iPPG.

Cardiovascular diseases are the leading cause of mortality worldwide and impose a tremendous burden on socio-economic costs. At present, prevention strategies based on personalized lifestyle interventions are considered the best way to contain the epidemics of these diseases. In this view, availability of innovative technological systems able to assist people in self-monitoring and self-coaching is expected to play a crucial role. Aiming to this, we have developed a multi-sensing device, called Wize Mirror which has the appearance of a conventional mirror. The Wize Mirror allows the users to self-monitor their cardio-metabolic risk also providing personalized lifestyle guidance. The mirror has undergone a validation study in three centers to assess the coherence of mirror measurements with standard clinical tests and to evaluate its acceptability by potential users. In this paper, following a summary of the major functionalities of the mirror, we report on the analysis of data about system acceptability assessed during the validation study. Acceptability was measured by means of the System Usability Scale, which resulted in ”good” usability of the Wize Mirror.

The quality of life and individual well-being are universally recognised as key factors in disease prevention. In particular, lifestyle interventions are effective tools for reducing the risk and incidence of major illnesses, such as cardiovascular diseases and metabolic disorders. On the other hand, patient role is progressively shifting from being a passive recipient of care towards being a co-producer of her/his health. In this frame, novel devices and systems able to help individuals in self-evaluation are expected to play a crucial role. In this special issue we focus on innovative methodologies and technologies devoted to individual self-assessment, oriented both to healthy people to maintain their well-being, and to diseased persons to improve their care.

BACKGROUNDSeveral studies have focused on the role of epicardial fat in the pathogenesis of cardiovascular disease (CVD). The main purpose of the study was to evaluate a computerized method for the quantitative analysis of epicardial fat volume (EFV) by non-contrast cardiac CT (NCT) for coronary calcium scan and coronary CT angiography (coronary CTA).METHODSThirty patients (61±12.5 years, 73% male, body mass index (BMI) =25.9±6.3 kg/m2) referred to our Institution for suspected coronary artery disease (CAD) underwent NCT and coronary CTA. Epicardial boundaries were traced by 2 experienced operators (operator 1, operators 2) on 3 and 6 short-axis (SA) slices. EFV was computed with a semi-automatic method using an in-house developed software based on spherical harmonic representation of the epicardial surface. In order to analyze the inter-observer variability both the Coefficient of Repeatability (CR) and Intra Class Correlation (ICC) were computed.RESULTSThe total EFV was 103.62±50.97 and 94.96±67.91 cc in NCT and coronary CTA with non-significant difference (P=0.292). CR error was 10.22 cc for operator 1 and 11.31 cc for operator 2 in NCT and 7.99 cc for operator 1 and 7.75 cc for operator 2 in coronary CTA. To analyze the inter-observer variability CR and ICC were computed. CR was 8.17 and 8.39 cc with NCT and 7.07 and 7.21 cc with CTA for 6 and 3 SA slices respectively. ICC values >0.99 were obtained in all cases. The right ventricular EFV was 67.23±31.4 and 57.41±34.3 cc for NCT and coronary CTA respectively; the corresponding values for left ventricular EFV were 38.01±19.1 and 35.27±25.9 cc.CONCLUSIONSBoth NCT and coronary CTA can be used with low intra- and inter-observer variability for computer-assisted measurements of EFV. Cardiac CT may allow a fast and reliable computation of EFV in clinical setting.

A person's well-being status is reflected by their face through a combination of facial expressions and physical signs. The SEMEOTICONS project translates the semeiotic code of the human face into measurements and computational descriptors that are automatically extracted from images, videos, and three-dimensional scans of the face. SEMEOTICONS developed a multisensory platform in the form of a smart mirror to identify signs related to cardio-metabolic risk. The aim was to enable users to self-monitor their well-being status over time and guide them to improve their lifestyle. Significant scientific and technological challenges have been addressed to build the multisensory mirror, from touchless data acquisition, to real-time processing and integration of multimodal data.

The face reveals the healthy status of an individual, through a combination of physical signs and facial expressions. The project SEMEOTICONS is translating the semeiotic code of the human face into computational descriptors and measures, automatically extracted from videos, images, and 3D scans of the face. SEMEOTICONS is developing a multisensory platform, in the form of a smart mirror, looking for signs related to cardio-metabolic risk. The goal is to enable users to self-monitor their well-being status over time and improve their life-style via tailored user guidance. Building the multisensory mirror requires addressing significant scientific and technological challenges, from touch-less data acquisition, to real-time processing and integration of multimodal data.

ICT solutions to foster behavioural change have been shown to be effective in implementing primary prevention in terms of a healthy lifestyle. Primary prevention is the most viable approach to reduce the socio-economic burden of chronic and widespread diseases, such as cardiovascular and metabolic diseases. In this paper, we present a novel multisensory device, the Wize Mirror, which is under development in the EU FP7 Project SEMEOTICONS. The Wize Mirror detects and monitors over time semeiotic face signs related to cardio-metabolic risk, and encourages users to reduce their risk by improving their lifestyle.

What if you could discover your health status by looking at yourself in the mirror? Since November 2013, the EU FP7 Project SEMEOTICONS is working to make this possible. The Project is building a multi-sensory device, having the form of a conventional mirror, able to read the semeiotic code of the face and detect possible evidence of the onset of cardio-metabolic diseases. The device, called Wize Mirror, integrates unobtrusive imaging sensors used to capture videos, images and 3D scans of the face. These are processed to assess the risk of a cardio-metabolic disease and thereby suggest possible strategies to prevent its onset.

In modern medicine signs derivable from face observation remain an important part of the physical examination that, together with the anamnesis, constitutes the basis for a rational decision-making. Therefore, face semiotics may be considered as a potential source of information for obtaining markers of obesity, metabolomics, cardiovascular homeostasis and psychophysical status. Once properly mapped to computational descriptors, their systematic exploitation is expected allowing the building of effective self-monitoring systems. In this perspective, in the frame of the FP7 project SEMEOTICONS, the most relevant face signs of cardio-metabolic risk are reviewed and analysed so as to drive their detection, quantification and integration into a virtual individual model useful for cardio-metabolic risk prevention.

Cardiovascular and metabolic diseases are the major causes of morbidity and mortality in the Western Countries. Metabolic syndrome (defined as 3 out of 5 factors among increased waist circumference, hypertension, high blood glucose, high triglyceride and low high density lipoprotein (HDL) cholesterol concentrations) is associated with increased cardiometabolic risk. Other indexes have been proposed and validated, based on the measurement of plasma concentration of lipids, glucose and liver enzymes. In the SEMEOTICONS project we plan to measure parameters related to increased cardiometabolic risk, e.g. skin accumulation of cholesterol and advanced glycated end products, liver enzyme alteration by changing in skin and eye color and obesity. The results will allow to evaluate cardiometabolic risk using non invasive clinical parameters. The new score obtained will be compared with previously validated indexes. In this paper we have evaluated the most common cardiometabolic risk scores i.e., VAI (Visceral Adiposity Index), HTG- Waist (Hypertriglyceridemic Waist), FLI (Fatty Liver Index) and LAP (Lipid Accumulation Product), that we will use during the project.

Background: In 2010, the International Atomic Energy Agency launched the “3A’s campaign” as an effective tool for primary cancer prevention. In 2011, the American Association of Physicists in Medicine recommended the size specific dose estimate (SSDE). Objectives: To audit doses of Coronary CT Angiography (Coronary CTA) in tertiary care referral center. Methods: We reviewed 998 consecutive Coronary CTA (from 2007 to 2012). Doses (CTDIvol mGy), DLP (mGy*cm), effective dose (DLP*0.014, mSv) were on-line archived. SSDE was estimated retrospectively. Appropriateness score was evaluated for exams performed from the 2010. Results: Overall median dose per Coronary CTA was 49.7 mGy for CTDIvol, 55.5 mGy for SSDE, 994.96 mGy*cm for DLP, 13.9 mSv for effective dose. Median DLP decreased over time (1452.94 in 2007, 1605.56 in 2008, 1113.49 in 2009, 759.99 in 2010, 448.61 in 2011 and 497.88 mGy*cm in 2012, p 1 (88%) CTDIvol underestimated SSDE (48.49 vs 57.19 mGy), whilst in patients with SDF < 1 (12%) CTDIvol overestimated SSDE (56.46 vs 50.3 mGy). Scans were appropriate in 58%, uncertain in 24%, and inappropriate in 18% of cases. Doses were similar in appropriate, uncertain or inappropriate examinations and in excellent-to-good (81%) vs. sufficient-to-poor (19%) image quality exams. Conclusions: Coronary CTA reference doses can be very misleading. SSDE can allow individual technique optimization. The dose is similar in appropriate and inappropriate examinations, and unrelated to image quality. The rate of inappropriate examinations is still too high even after dissemination of guidelines.

Facial expressions are visible signs of the affective and psychological state of a person, which is strictly correlated with the pathogenesis of clinically relevant diseases and more in general with individuals' wellbeing. The main idea highlighted in this paper is the exploitation of the facial expression analysis for wellbeing monitoring and self-management. This will occur by an innovative multisensory device that will be able to collect images and signals, extract quantitative features of facial expression related to stress, anxiety and fatigue and map them to computational descriptors of an individual's wellbeing. The latter phase will be based on a virtual individual's model conceived to allow the computation and tracing of the daily evolution of individual's wellness. Personalized advices and coaching messages will support the user in keeping a healthy lifestyle and counteract potentially harmful behaviours. The work is part of the FP7 STREP SEMEOTICONS project whose application field will be the prevention of cardio-metabolic risk, for which healthcare systems are registering an exponential growth of social costs.