"The Internet of Things (IoT) is creating a new structure of awareness – a cybernetic one – upon physical processes. Industries of different kinds are expected to join soon this revolution, leading to the so-called Factories of the Future or Industry 4.0. Our considered IoT-based industrial cyber-physical system (CPS) works in three generic steps: 1) Large data acquisition / dissemination: A physical process is monitored by sensors that pre-process the (assumed large) collected data and send the processed information to an intelligent node (e.g. aggregator, central controller); 2) Big data fusion: The intelligent node uses artificial intelligence (e.g. machine learning, data clustering, pattern recognition, neural networks) to convert the received (""big"") data to useful information to guide short-term operational decisions related to the physical process; 3) Big data analytics: The physical process together with the acquisition and fusion steps can be virtualized, building then a cyber-physical process, whose dynamic performance can be analysed and optimized through visualization (if human intervention is available) or artificial intelligence (if the decisions are automatic) or a combination thereof. We will focus on how to optimize the prediction, detection and respective interventions of rare events in industrial processes based on these three steps. Our proposed general framework, which relies on an IoT network, aims at ultra-reliable detection / prevention of rare events related to a pre-determined industrial physical process (modelled by a particular signal). The framework will be process-independent, but the actual solution will be designed case-by-case. We will consider the CPS working as a complex system so that these three steps, which operate with relative autonomy, are strongly interrelated. For example, the way the sensors measure the signal related to the physical process will affect what is the best data fusion algorithm, which in turn will generate a certain awareness of the physical process that will form the basis of the proposed data analytics procedure. As proof-of-concept, our approach will be applied to predictive maintenance in an automotive industrial plant from SEAT in Spain, in the Nokia base-station factory at Oulu and in the LUT laboratory of control engineering and digital systems. "

The WAge project will develop and validate the first comprehensive framework for assessing and understanding the roles and interactions between physical and psychosocial risk factors across age groups through robust modelling and policy-relevant evidence gathering. Through the proposed framework, the project will pave the way for designing, implementing and validating effective multi-level intervention strategies and policy changes for workers of all ages at the individual and organizational level. The project focuses on a question that has high societal relevance and is timely because the proportion of employees under adverse physical and psychosocial work environments is likely to increase as organisations, businesses, and workers have to adapt to post-pandemic working environments in an ageing Europe. WAge is proposing a concept that is addressing the factors related to the health and overall wellbeing of workers across age groups. One of the main aims of the proposed action is the generation of integral, policy-relevant evidence that are vital for the improvement and update of occupational health but also for this HORIZON Europe call. In WAge, the gathering and utilization of evidence within decision-making spaces will be based on representation and accountability of policymaking, bringing multiple perspectives and knowledge through democratic participation into decision-making processes across the project, from data collection, and management, to analysis, and implementation.

MOLIERE - "MObiLIty sERvices Enhanced by GALILEO & Blockchain" will build the world's best open data commons for mobility services, the “Wikipedia of public transport and new mobility data”, a Mobility Data Marketplace (MDM) underpinned by blockchain technology, raising the profile, visibility, availability, and utility of geo-location data from GALILEO, and will test it to fuel and demonstrate a diverse set of concrete, highly relevant mobility scenarios and use cases where geo-location data is key, addressing the needs of cities, public transport authorities, mobility service providers, and end-users.

SHAREWORK‘s main objective is to endow an industrial work environment of the necessary “intelligence” and methods for the effective adoption of Human Robot Collaboration (HRC) with not fences, providing a system capable of understanding the environment and human actions through knowledge and sensors, future state predictions and with the ability to make a robot act accordingly while human safety is guaranteed and the human-related barriers are overcome. SHAREWORK will develop the needed technology for facing the new production paradigm compiling the necessary developments in a set of modular hardware, software and procedures to face different HRC applications in a systematic and effective way. A knowledge base (KB) to include system “know-how” data as well as real-time environment information is developed. An environment run-time perception and cognition updates this KB with object detection, human tracking and task identification. A human-aware dynamic task planning system will react based on previous knowledge and environment status by reassigning tasks and/or reconfiguring robot control. This data will allow robot intelligent motion planners to control robots while safety is ensured by a continuous ergonomics and risk assessment module to face a safety-productivity trade-off. A multimodal human-robot communication system will provide interfaces for bidirectional communication between operator and robot. Finally, methods for overcoming human-related barriers and data reliability and security concerning the entire framework are applied for a successful integration in the industry. SHAREWORK technology will be demonstrated in four different industrial cases: for railway, automotive, mechanical machining and equipment goods sectors. The usability of the developed HRC solutions in different industrial sectors and company sizes will increase productivity, flexibility, and reduce human stress, to support the workers and to strengthen European industry.