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

Smart information desk system with voice assistant for universities

This article aims to develop a smart information desk system through a smart mirror for universities. It is a mirror with extra capabilities of displaying answers for academic inquiries such as asking about the lecturers’ office numbers and hours, exams dates and times on the mirror surface. In addition, the voice recognition feature was used to answer spoken inquiries in audio responds to serve all types of users including disabled ones. Furthermore, the system showed general information such as date, weather, time and the university map. The smart mirror was connected to an outdoor camera to monitor the traffics at the university entrance gate. The system was implemented on a Raspberry Pi 4 model B connected to a two-way mirror and an infrared (IR) touch frame. The results of this study helped to overcome the problem of the information desk absence in the university. Therefore, it helped users to save their time and effort in making requests for important academic information

Methods for acquisition and integration of personal wellness parameters

Wellness indicates the state or condition of being in good physical and mental health. Stress is a common state of emotional strain that plays a crucial role in the everyday quality of life. Nowadays, there is a growing individual awareness of the importance of a proper lifestyle and a generalized trend to become an active part in monitoring, preserving, and improving personal wellness for both physical and emotional aspects. The majority studies in this field relies on the evaluation of the changes of sensed parameters passing from rest to “maximal” stress. However, the vast majority of people usually experiences stressing circumstances in everyday life. This led us to investigate the impact of mild cognitive activation which can be somehow comparable to usual situations that everyone can face in daily life. Several signals and data can be useful to characterize the state of a person, but not all of them are equally important. So it is crucial to analyse the mutual relevance of the different pieces of information. In this work we focus on a subset of well-established psychophysical descriptors and we identified a set of devices enabling the measurement of these parameters . The design of the experimental setup and the selection of sensing devices were driven by qualitative criteria such as intrusiveness, reliability, and ease of use. These are deemed crucial for implementing effective (self-)monitoring strategies. A reference dataset, named “Mild Cognitive Activation” (MCA), was collected. The last aim of the project was the definition of a quantitative model for data integration providing a concise description of the wellness status of a person. This process was based on unsupervised learning paradigms. Data from MCA were integrated with data from the “Stress Recognition in Automobile Drivers” dataset . This allowed a cross validation of the integration methodology

Semeoticons -Reading the face code of cardio-metabolic risk

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