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This article introduces an immersive Virtual Reality (VR) application designed to assess the interaction capabilities of users with physical and cognitive limitations, including older adults and individuals with disabilities, as well as ICU patients. The VR application encompasses six tasks varying in complexity, each designed to evaluate different aspects of VR interaction skills, such as movements of the head, arms, and fingers, alongside more intricate activities like pick-and-place, pointing, and painting.The paper details the VR application's specifications, including its system architecture, deployment framework, and data structure. The application's efficacy was tested through three pilot studies in a retirement home setting. The analysis focused on examining correlations among various factors, including age, cognitive abilities (evaluated using the Mini-Mental Status Examination), and previous VR experience. The findings reveal significant correlations, illuminating the effects of age, cognitive capacity, and past VR interactions on task performance. The results emphasize the importance of accounting for user-specific attributes, prior experiences, and cognitive abilities in the design of VR-based therapeutic interventions.

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This paper explores the use of Robotics and decentralized Multi-Agent Reinforcement Learning (MARL) for side-by-side navigation in Intelligent Wheelchairs (IW). Evolving from a previous work approach using traditional single-agent methodologies, it adopts a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to provide control input and enable a pair of IW to be deployed as decentralized computing agents in real-world environments, discarding the need to rely on communication between each other. In this study, the Flatland 2D simulator, in conjunction with the Robot Operating System (ROS), is used as a realistic environment to train and test the navigation algorithm. An overhaul of the reward function is introduced, which now provides individual rewards for each agent and revised reward incentives. Additionally, the logic for identifying side-by-side navigation was improved, to encourage dynamic alignment control. The preliminary results outline a promising research direction, with the IWs learning to navigate in various realistic hallways testing scenarios. The outcome also suggests that while the MADDPG approach holds potential over single-agent techniques for the decentralized IW robotics application, further investigation are needed for real-world deployment.

Abstract
The increasing number of IoT deployment scenarios and applications fostered the development of a multitude of specially crafted communication solutions, several proprietary, which are erecting barriers to IoT interoperability, impairing their pervasiveness. To address such problems, several middleware solutions exist to standardize IoT communications, hence promoting and facilitating interoperability. Although being increasingly adopted in most IoT systems, it became clear that there was no one size fits all solution that could address the multiple Quality-of-Service heterogeneous IoT systems may impose. Consequently, we witness new interoperability challenges regarding the usage of diverse middleware. In this work, we address this issue by proposing a novel architecture - the PolyglIoT, that can effectively interconnect diverse middleware solutions while considering the delivery QoS requirements alongside the proposed translation. We analyze the performance and robustness of the solution and show that such Multiprotocol Translator is feasible and can achieve a high performance, thus becoming a fundamental piece to enable future highly heterogeneous IoT systems of systems.

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Electromobility is a critical component of Europe’s strategy to create a more sustainable society and support the European Green Transition while enhancing quality of life. Electrification also plays an important role in securing Europe’s position in the growing market of electric and autonomous vehicles (EAV). The EU-funded OPEVA project aims to take a big step towards deployment of sustainable electric vehicles by means of optimising their support in an ecosystem. Specifically, the project focuses on analysing and designing optimisation architecture, reducing battery charging time, and developing infrastructure, as well as reporting on the driver-oriented human factors. Overall, OPEVA’s goal is to enhance EAV market penetration and adoption, making them more accessible and convenient. The aim of this paper is to inform the European automotive, transportation, energy and mobility community be presenting the OPEVA manifestation, and the overall solution strategy solidified through the progress throughout the first year of the project.

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