The incidence of undiagnosed diabetes accounts for 36% European adults, while 541M adults worldwide have Impaired Glucose Tolerance (IGT), an important risk factor for further T2D development. Both IGT and/or Impaired Fasting Glucose (IFG) are intermediate glucose mishandling (i.e. intermediate conditions in the healthy-T2D transition) and are manifestations of the so-called prediabetes condition. Prediabetes itself is not an extensively studied condition compared to the overt T2D, but it is also a condition that can be reversed without the prescription usage to not proceed into T2D. The aim of our project is to develop a prototype tool for the real-time prediction of the prediabetic risk based on a series of patient-specific mathematical models (firstly developed during the FP7 MISSION-T2D project) that simulate metabolism, pancreas hormone production, microbiome metabolites, inflammatory process and immune system response. The prediction algorithm will be based on a “physics-informed machine learning” approach. A rich dataset of real-life data will be combined with a mathematical model to overcome the limits of a “black-box” ML approach, while reducing the computational time for simulating the solutions of a heavy mathematical models and improving its prediction performances.We will collect the necessary training data (e.g., diet questionnaire, physical activity, blood metabolites and microbiome) from already existing clinical studies (used as retrospective trials) which are representative of the real-life scenarios of a prediabetes/diabetes risk insurgence in adulthood (20-80y): family history, Metabolic Syndrome, Liver disease and obesity. A newly dedicated multicentric pilot prospective observational study will be also performed, during which we will also equip the participants with wearable sensors (e.g. glucose monitoring, bioimpedance, heart rate, accelerometer).

The main objective of TRINITY is to create a network of multidisciplinary and synergistic local digital innovation hubs (DIHs) composed of research centers, companies, and university groups that cover a wide range of topics that can contribute to agile production: advanced robotics as the driving force and digital tools, data privacy and cyber security technologies to support the introduction of advanced robotic systems in the production processes. The result will be a one-stop shop for methods and tools to achieve highly intelligent, agile and reconfigurable production, which will ensure Europe’s welfare in the future. The network will start its operation by developing demonstrators in the areas of robotics we identified as the most promising to advance agile production, e.g. collaborative robotics including sensory systems to ensure safety, effective user interfaces based on augmented reality and speech, reconfigurable robot workcells and peripheral equipment (fixtures, jigs, grippers, …), programming by demonstration, IoT, secure wireless networks, etc. These demonstrators will serve as reference implementation for two rounds of open calls for application experiments, where companies with agile production needs and sound business plans will be supported by TRINITY DIHs to advance their manufacturing processes. Besides technology-centered services, primarily laboratories with advanced robot technologies and know-how to develop innovative application experiments, TRINITY network of DIHS will also offer training and consulting services, including support for business planning and access to financing. Services of participating DIHs and dissemination of information to wider public will be provided through a digital access point that will be developed in the project. Another important activity of the project will be the preparation of a business plan to sustain the network after the end of the project funding.

Motivation for the study: A growing body of evidence suggests that integrated technologies of brain-computer interfaces (BCI) and virtual reality (VR) environments provide a flexible platform for a series of neurorehabilitation therapies, including significant post-stroke motor recovery and cognitive-behavioural therapy. When immersed in such an environment, the subject's perceptual level of social interaction is often impaired due to the sub-optimal quality of the interface lacking the social aspect of human interactions. Project objective: We propose a user-friendly wearable low-power smart BCI system with an ecologically valid VR environment in which both the patient and therapist collaboratively interact via their person-specific avatar representations. On the one hand, the patient voluntarily, and in a self-paced manner, manages their activity in the environment and interacts with the therapist via a BCI-driven mental imagery process. This process is computed and rendered in real-time on an energy efficient wearable device. On the other hand, the therapist's unlimited motor and communication skills allow him to fully control the environment. Thus, the VR environment may be flexibly modified by the therapist allowing for different occupational therapy scenarios to be created and selected following the patient's recovery needs, mental states, and instantaneous responses. Implementation: Careful attention will be paid to balance known neurophysiological evidence of the process with artificial intelligence (AI) within the active BCI protocols to avoid running into conceptual pitfalls. Computed features of EEG signals will serve to monitor the patient's engagement, cognitive workload, or mental fatigue in real-time. These indicators will be combined with observable patient’s performance and behaviours to improve the accuracy of mental state estimation. Exceeding critical mental state levels will signal the therapist to activate appropriate countermeasures in the form of environmental and task changes. Research and technological challenges: To challenge and overcome existing technologies, commercially available head-mounted VR displays (HMD) combined with miniaturized energyefficient microcontroller units will be employed for EEG signal processing, BCI discrimination and on-board classification implementation, and a full-duplex communication with the HMD controllers. Advanced dry EEG sensors suitable to operate and be placed on the scalp without interfering with the HMD will be developed and tested. A novel patient-to-therapist multimodal collaborative environment augmented through VR immersion and by AI monitored patient’s brain activity will be created. By combining these pieces, a low-power wearable BCI-HMD system will be constructed. A series of clinical studies will validate the system.

EdgeAI is as a key initiative for the European digital transition towards intelligent processing solutions at the edge. EdgeAI will develop new electronic components and systems, processing architectures, connectivity, software, algorithms, and middleware through the combination of microelectronics, AI, embedded systems, and edge computing. EdgeAI will ensure that Europe has the necessary tools, skills, and technologies to enable edge AI as a viable alternative deployment option to legacy centralised solutions, unlocking the potential of ubiquitous AI deployment, with the long-term objective of Europe taking the lead of Intelligent Edge. EdgeAI will contribute to the Green Deal twin transition with a systemic, cross-sectoral approach, and will deliver enhanced AI-based electronic components and systems, edge processing platforms, AI frameworks and middleware. It will develop methodologies to ease, advance and tailor the design of edge AI technologies by co-ordinating efforts across 48 of the brightest and best R&D organizations across Europe. It will demonstrate the applicability of the developed approaches across a variety of vertical solutions, considering security, trust, and energy efficiency demands inherent in each of these use cases. EdgeAI will significantly contribute to the grand societal challenge to increase the intelligent processing capabilities at the edge.