In spite of the huge number of pathogens and diseases, the number of “transmission channels” or “transport routes” for microorganisms to enter in a farm or any other Farm2Fork facility is indeed limited to just nine. Tracing or monitoring transport of a single or a few microorganisms using a particular channel will provide valuable information on the channel itself and therefore on the transport effectiveness for the whole set of microorganisms using that particular channel. HE-FARM will develop and validate a methodology -based on experimental assays and tests in lab & operational env- to assess and predict transport-channel resolved biosecurity and simultaneously increase maturity of several disruptive novel techs. A software/APP will be created to help with these procedures and proposed as a draft for a future EN standard. Prototypes of novel technologies for biosecurity will additionally be developed and validated in operational environment -including extensive and intensive cow, pig, chicken, turkey, sheep and snail farms, a slaughter and meat preparation plant and trucks: Fast Integrated air-borne virus smart detector (PRRS and Avian Flue but easily extendable to other virus with an enormous like African Swine Fever whose early detection in air in minutes will have an enormous impact) Sanitization by low-toxicity biocides & dynamic aggregation. Low-toxicity insecticides & repellents & dynamic aggregation application & Env-Friendly Insect and Arachnids barrier and prevention techs. Rapid Vehicle decon. station. Biosecure & Env-Friendly Hall & Heat Venting and Cooling. Portable Low-cost test-device for fast measuring microbiological metabolism. Cold plasma farm water sanitization. Usage and training procedures and manuals & assessment of performance method. Finally at least other 3 biosecure techs externally provided selected in an open call will be experimentally tested. Comm. & dissemination include liaising with authorities like EFSA and OIE.

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.

Neck and low back pain (NLBP) are leading causes for years lived with disability in Europe and worldwide. About 70% of all adults experience NLBP at some point in their lives, and both conditions are among the top ten in terms of overall disease burden expressed as disability adjusted life years. Management of NLBP is a difficult challenge for healthcare professionals since their decisions have a decisive impact on the patient’s future health and welfare, as well as on the economic burden on the public and private healthcare systems. However, health professionals often lack appropriate information to tailor the management and follow-up of individual patients and to predict the outcome of a certain treatment. At European level, diverse research initiatives are undergoing at this moment for tackling NLBP from diverse angles, including biomarkers (PainOmics), pain self-management (selfBACK), lifestyle and workplace conditions (AHA), or patients stratification (STarT Back). Back-UP project provides a wider vision of NLBP, bringing together the research groups that are leading these and other innovative approaches to create a prognostic model to underpin more effective and efficient management of NLBP based on the digital representation of multidimensional clinical information and on simulations of the outcomes of possible interventions. Patient-specific models will provide a personalised evaluation of the patient case, using multidimensional health data from the following sources: personal, health, psychological, behavioural, and socioeconomic factors related to NLBP; biological patient characteristics, including musculoskeletal structures and function, and molecular data; and workplace and lifestyle risk factors. Back-UP will provide health, well-being and economic benefits to different user profiles (clinicians, employers / insurance companies and patients) and will create a channel for sharing information during the rehabilitation and return to work process.

The MANiBOT research aims at bi-manual mobile robots, able to perform a wide variety of manipulation tasks with highly diverse objects, possibly partly or fully unknown beforehand, in a human-like manner and performance. To achieve this, we advance and fuse all necessary technologies, from multimodal perception, cognition and control, to novel cognitive mechatronics. We develop new environment understanding and object/pose recognition methods, empowered through adaptive, context-aware fusion of vision, proximity and tactile sensing and emphasizing on adequate efficiency for fast and effective manipulation, even of objects without precise model, including deformable ones, and in diverse, challenging environments with human presence. We also develop a novel suite of manipulation primitives including non-prehensile manipulations, which along with bi-manual manipulation will allow the transfer of diverse objects with various sizes, weights, shapes, materials and rigidities from a mobile robot, with performance close to that of humans, even upon significant spatial constraints. The above are fused through a novel approach for robot cognitive functions based on multi-level robot cycles that allow learning, composing and swiftly adapting robot behaviors for complex manipulations, covering key topics of sequential manipulation of multiple objects to achieve complex goals. We push the limits of physical intelligence of bimanual mobile robots by coupling our methods with novel cognitive mechatronics, fusing advanced tactile and proximity sensors with a bi-manual mobile manipulator, optimized for energy efficiency and increased autonomy, including HRI capabilities for trustworthy and efficient operation. Our outcomes will be evaluated in four use cases, in three pilot sites (TRL5), in challenging environments where the handling of abundance of different objects is needed; in retail/supermarkets and transport/airports, for shelves restocking and baggage handling operations.