Ageing@Work will develop a novel ICT-based, personalized system to support ageing workers (aged 50+) into designing fit-for-purpose work environments and managing flexibly their evolving needs. Advanced dynamically adapted virtual models of workers will incorporate specificities in respect to skills, physical, cognitive and behavioral factors, being extended from the work context to personal life aspects interacting with workability, health and well-being. Virtual workplace models will encode characteristics of the workplace (factory, outdoor work site, home), at both physical and semantic, resource/process levels. On top of the models, computational intelligence will be responsible to (a) assess user specificities and needs i.r.t. work conditions, both in terms of ergonomics, health and safety issues and task assignments, and (b) perform personalized predictive simulations on workability, health and well-being. Recommendations will then be provided both to the worker and company (under strict privacy restrictions), on how the working conditions must adapt. The worker models will be populated by highly unobtrusive worker sensing, both at work, at home and on the move. To foster workability and productivity, highly personalized, intuitive, age-friendly productivity, co-design enhancement tools will be developed, including ones for AR/VR-based context-awareness and telepresence, lifelong learning and knowledge sharing. On top of these, a novel Ambient Virtual Coach (AVC) will encompass an empathic mirroring avatar for subtle notifications provision, an adaptive Visual Analytics –based personal dashboard, and a reward-based motivation system targeting positive and balanced worker behavior at work and personal life, towards a novel paradigm of ambient support into workability and well-being. The integrated system will be developed by user-centered design and will be evaluated at two pilot sites, related to core Industry 4.0 processes of mining and machines production.

Cancers of the Head and Neck Region (HNC) are the 6th more deadly cancers worldwide: in Europe ~150.000 new cases are detected and ~70.000 patients die every year. The main reasons for high mortality are the fact that the majority of cases are diagnosed in advanced Stage and the intrinsic heterogeneity of such tumors. At present the only adopted treatment decision method is based on TNM (Tumor-lymph-Nodes-Metastasis) prognostic system, that considers only a few risk factors such as smoking, alcohol abuse and more recently HPV. The TNM system is therefore inadequate to capture the patient-specific biomolecular characteristics of the tumor. HNC treatments can have hard impact on patient’s aesthetics and functionalities and, due to their toxicity, can cause severe morbidity and greatly deteriorate patient’s quality of life. A more precise prognostic prediction than the current TNM system is needed that allows implementing the first-line treatment that maximizes the therapeutic result and minimizes the impacts of therapy. BD2Decide DSS provides clinicians with the "means" and all the necessary information to tailor treatment and care delivery pathway to each and any HNC patient during their usual practice, in contrast to current “one-size-fits-all approach”. BD2Decide realizes and validates an Integrated Decision Support System that links population-specific epidemiology and behavioral data, patient-specific genomic, pathology, clinical and imaging data with big data techniques, multiscale prognostic models. Advanced graphical visualization tools are developed for prognostic data disclosure and patient co-participation to the selected treatment. BD2Decide will improve the clinical decision process, uncover new patient-specific patterns that can improve care, and create a virtuous circle of learning. A multicentric clinical study with more than 1.000 patients will be used to validate the system.

The first and core objective of City4Age is to enable Ambient Assisted Cities or Age-friendly Cities, where the urban communities of elderly people living in Smart Cities are provided with a range of ICT tools and services that - in a completely unobtrusive manner - will improve the early detection of risks related to cognitive impairments and frailty while they are at home or in the move within the city. The second objective is to provide a range of associated tools and services which - with the appropriate interventions - will mitigate the detected risks. The final objective of C4A is to define a model which will provide sustainability and extensibility to the offered services and tools by addressing the unmet needs of the elderly population in terms of (i) detecting risks related to other health type problems, (ii) stimulating and providing incentives to remain active, involved and engaged, (iii) creating an ecosystem for multi-sided market by matching needs and their fulfillments, (iv) contributing to the design and operation of the ultimate Age-friendly City, where the city itself provides support for detecting risks and providing interventions to those affected by mild cognitive impairment (MCI) and frailty. To achieve these objectives City4Age builds on: - behavioural, sociological and clinical research on “frailty” and MCI in the elderly population; - state of art ICT technology (i) for “sensing” personal data and exposing them as linked open data, (ii) for designing the algorithms and the API’s to extract relevant behaviour changes and correlated risks, and (iii) for designing interventions to counter the risks, - stakeholder engagement in order to be driven by relevant user needs to ensure end-user acceptance.

Head and neck cancer can take away a patient’s “right to feel human,” and its impact on physical appearance, physical functioning, psychological status and general quality of life (QoL) can be devastating. Over the past several decades, the number of patients who survive head and neck cancer (HNC) has increased; this makes lifelong surveillance critical. HNC imposes an extremely high socioeconomic burden on patients during and after cancer compared to other tumors, including costs from treatment-induced morbidities, loss of workforce participation and short-term disability. Current survivorship care plans mostly focus on functional and health conditions of treated patients, whereas socioeconomic determinants of quality of life are often neglected due to difficult data collection. The widespread technologies for social communication and unobtrusive personal monitoring embedded in smartphones and object we commonly use and in our living environments have the potential to unobtrusively collect wealth of indicators of individual QoL. BD4QoL objective is to improve HNC survivor’s Quality of Life through person-centred monitoring and follow-up plan by contribution of artificial intelligence and big data unobtrusively collected from commonly used mobile devices, in combination with multi-source clinical, -omic, socioeconomic data and patients reported outcomes, to profile HNC survivors for pBD4QoL objective is to improve HNC survivor’s Quality of Life through person-centred monitoring and follow-up plan by contribution of artificial intelligence and big data unobtrusively collected from commonly used mobile devices, in combination with multi-source clinical, -omic, socioeconomic data and patients reported outcomes, to profile HNC survivors for personalized monitoring and support. The analysis of QoL indicators collected over time will allow to early detect risks, prevent long-term effects of treatment and inform patients and caregivers for personalized interventions.

PROTECT-CHILD is a project that aims to improve the outcomes of rare pediatric transplant patients by integrating multiple sources of high-throughput data from registries, hospital-based and public repositories, complying with ongoing initiatives such as the European Open Science Cloud (EOSC) and the European Health Data Space (EHDS). The project focuses on the co-design of a secure and privacy-preserving infrastructure, harmonization of data standards, and creation of a public/private infrastructure for assembling large datasets to improve clinical outcomes. The project involves top-level expertise from a consortium of technology specialists, data standardization experts, and High-Performance Computing (HPC) centers, as well as clinical experts, legal experts, patients’ representatives, and policy makers. The project is aligned with EHDS and General Data Protection Regulation (GDPR) principles and aims to empower secure and compliant processing, analysis, and sharing of sensitive personal data, including genomics, while preserving data privacy and security.