Real-time performance is critical for Augmented Reality (AR) systems as it directly affects responsiveness and enables the timely rendering of virtual content superimposed on real scenes. In this context, we present the DARLENE wearable AR system, analysing its specifications, overall architecture and core algorithmic components. DARLENE comprises AR glasses and a wearable computing node responsible for several time-critical computation tasks. These include computer vision modules developed for the real-time analysis of dynamic scenes supporting functionalities for instance segmentation, tracking and pose estimation. To meet real-time requirements in limited resources, concrete algorithmic adaptations and design choices are introduced. The proposed system further supports real-time video streaming and interconnection with external IoT nodes. To improve user experience, a novel approach is proposed for the adaptive rendering of AR content by considering the user’s stress level, the context of use and the environmental conditions for adjusting the level of presented information towards enhancing their situational awareness. Through extensive experiments, we evaluate the performance of individual components and end-to-end pipelines. As the proposed system targets time-critical security applications where it can be used to enhance police officers’ situational awareness, further experimental results involving end users are reported with respect to overall user experience, workload and evaluation of situational awareness.

Digital accessibility is vital for ensuring equal access and usability for individuals with disabilities. However, addressing the unique challenges faced by individuals with disabilities in XR environments remains a complex task. This paper presents an ongoing accessibility framework designed to empower developers in creating inclusive XR applications. The framework aims to provide a comprehensive solution, addressing the needs of individuals with disabilities, by incorporating various accessibility features, based on XR accessibility guidelines, best practices, and state of the art approaches. The current version of the framework has focused on the accessibility of XR environments for blind or partially sighted users, enhancing their interaction with text, images, videos, and 3D artefacts. The proposed work lays the foundation for Extended Reality (XR) developers to easily encompass accessible assets. In this respect, it offers customizable text settings, alternative visual content text, and multiple user interaction control mechanisms. Furthermore, it includes features such as edge enhancement, interactive element descriptions with dynamic widgets, scanning for navigation, and foreground positioning of active objects. The framework also supports scene adaptations upon user demand to cater to specific visual needs.

Zero-defect manufacturing is one of the most promising strategies to mitigate failures within manufacturing processes, allowing industries to increase product quality efficiently and effectively. One of the challenges faced in the practical adoption of zero-defect manufacturing is that the most important aspect of manufacturing, people, is often neglected. Aiming to support shop floor operators, this work introduces a human-centric approach assisting them to become aware of defects in the production line and imminently reconfigure it. Our system comprises an Augmented Reality application that encompasses interfaces that dynamically adapt to different contexts of use and enable operators to interact naturally and effectively and reconfigure the manufacturing process. The system leverages the efficiency of the shop floor operators in monitoring and controling the production line they are working on, according to the task they are performing, and their level of expertise, to produce appropriate visual components. To demonstrate the versatility and generality of the proposed system we evaluated it in three different production lines, conducting cognitive walkthroughs with experts and user-based evaluations with thirty shop floor operators. The results demonstrate that the system is intuitive and user-friendly, facilitating operator engagement and situational awareness, enhancing operator attentiveness, and achieving improved operational outcomes.

Human Activity Recognition is a crucial task for surveillance systems that has seen great advancements with the emergence of Artificial Intelligence. At the same time, hardware advances have allowed for development of systems that operate in real-time. However real-time performance is still a far from solved problem for wearable devices when it comes to computer vision tasks such as activity recognition. In this paper a hybrid solution for Human Activity Recognition is proposed that exploits a lightweight method for on-device posture recognition and a more heavyweight activity recognition method executed on the cloud. The experimental evaluation for the activity recognition module indicates superior performance compared to existing methods and the lightweight posture method can predict satisfactorily the desired classes. The developed system offers a user-friendly Augmented Reality application that provides scene annotations to the user including the activity of the detected persons.