Stroke is one of the world’s leading causes of death and disability and can have profound consequences that require effective rehabilitation to support survivors’ recovery and improve their quality of life. Despite their value, traditional rehabilitation methods tend to be repetitive and lack variety, which challenges survivors’ motivation. Additionally, these rehabilitation sessions are usually solitary, leaving survivors to practice exercises alone, which can lead to physical setbacks and social isolation. Such isolation can further reduce enthusiasm for therapy, delay recovery, and affect their mental well-being. This work details an extended version from the International Workshop on eXtended Reality for Industrial and Occupational Supports (XRIOS), conducted at IEEE VR 2025. It proposes a collaborative Virtual Reality (VR) framework that aims to increase survivors’ motivation during rehabilitation. Through its collaborative nature, it can involve multiple users in the same virtual space, from stroke survivors to healthcare professionals, giving them a common goal that they must join forces to accomplish. Various serious games were designed through a series of activities focused on specific gestures related to the rehabilitation of the upper limbs, thus improving physical recovery and mental well-being. The design and development were guided by a human-centered methodology that included survivors and professionals, resulting in a user study with a total of 53 participants, 18 from a rehabilitation center. The results indicate that this collaborative VR tool effectively boosts motivation, social interaction, and engagement while maintaining an accessible and manageable level of physical and mental demand for stroke recovery, underscoring its suitability for stroke recovery.

Stroke affects millions of people worldwide, often resulting in severe physical and cognitive limitations that impact daily life. Rehabilitation is essential for improving stroke survivors' quality of life, yet traditional methods face significant limitations, including repetitive exercises, lack of personalization, and minimal social interaction. These constraints can lead to disengagement, isolation, and even depression, ultimately hindering recovery. To address these challenges, we propose ADAPT, a framework that enables healthcare professionals to personalize collaborative Virtual Reality (VR) serious games for stroke rehabilitation. It is possible to tailor rehabilitation sessions by adjusting key parameters, such as the number of participants, game selection and sequencing, session durations, and goal difficulty. Additionally, the system can incorporate familiar objects and locations to enhance emotional engagement. Realtime monitoring features provide these professionals with live streaming, adjustable viewpoints, and interactive assistance tools, ensuring continuous support and adaptation. Furthermore, data is collected and stored for performance analysis, enabling datadriven decision-making for future sessions. Developed through a Human-Centered Design (HCD) approach in collaboration with a renowned rehabilitation center in Portugal, the framework ensures usability and relevance in clinical practice. Initial testing has demonstrated positive results, with healthcare professionals praising the personalization capabilities and providing valuable feedback for further improvements. By integrating adaptive, collaborative, and engaging VR experiences, this framework aims to enhance stroke rehabilitation outcomes, increase survivors motivation, and foster social interaction, making rehabilitation more effective and personalized.

Collaborative Virtual Reality (VR) can be a powerful tool for enhancing the engagement, motivation, and social interaction of stroke survivors, significantly improving their quality of life and willingness to participate in the rehabilitation process. To ensure the success of these experiences, the active involvement of healthcare professionals is crucial, enabling them to better understand social dynamics and support stroke survivors' over time. Regardless, literature highlights a significant lack of tools to assist healthcare professionals in effectively monitoring and supporting such activities, particularly in ecological settings like rehabilitation centers. This work proposes REVIVE, a collaborative VR monitoring tool designed to enhance healthcare professionals' ability to oversee and adjust rehabilitation activities. The tool includes features such as live video streaming with adjustable perspectives, interactive annotations (e.g., directional arrows, drawings, text messages), and a virtual laser pointer. It also provides access to key session metrics and historical data, enabling professionals to tailor interventions and better understand the collaborative process of stroke survivors. Additionally, insights from two user studies are reported, involving over 60 participants, demonstrating REVIVE's potential to assist survivors, while improving how healthcare professionals manage and monitor rehabilitation involving collaborative VR scenarios.

Deep reinforcement learning is a dynamic field that has been successfully applied in various engineering and scientific disciplines. However, like many areas of automated learning, it presents a significant challenge: understanding its models, which can hinder human trust in the decisions made by these algorithms. To address this issue, we conducted a focus group and interviews with experts in machine learning and reinforcement learning to gain insights into the perceptions and preferences surrounding interpretability techniques and tools in reinforcement learning systems. The discussion of the focus groups used an interactive dashboard to simplify the analysis of reinforcement learning environments. The focus group results showed many problems to tackle with the current approaches regarding RL agent training, like data manipulation, environment representation and reward misinterpretation. The focus group discussion also presented some key features of a visualization approach to interpretability in RL: contextual presentation of information, integration with existing pipelines, and ease of distribution.

As smart technologies become increasingly embedded in daily life, the demand for natural and efficient human-machine interaction continues to grow. Multimodal systems, which allow interacting with multiple modalities, such as speech or gestures, potentially supporting a more natural and efficient way for users to communicate with machines. However, the complexity of these systems introduces significant challenges in development and evaluation. Nevertheless, even when interaction data are collected, these systems can generates large volumes of data, making it difficult to understand how users engage with the system, how the system responds, identify system limitations as well as uncover interaction patterns. In this sense, visualisation can play a crucial role in helping researchers and developers make sense of multimodal interaction data in order to reveal communication patterns, uncover system bottlenecks, and support deeper insight into system (and user) behavior. In this work we explore the potential of interactive visualisation, through a case study considering a multimodal system, and demonstrate how interactive visualisation can be useful to respond to a set of questions that we want to know about the system evaluation.

This work explored the impact of expert representation during the (re)use of Augmented Reality (AR) instructions for remote assistance.A user study with sixteen participants compared three conditions: C1-no agent, C2-human agent, and C3-virtual agent, within a real-world maintenance scenario.Results indicate that reusing AR-instructions was well accepted, even when not perfectly aligned with the task context.While C2 reported higher levels of social presence and trust, C3 was considered the best option when reusable content was used and limited interaction was required.This allowed to focus more on the task and less on the agent representation.

Abstract Remote collaboration using eXtended Reality (XR) has been explored to establish a common ground between physically distributed individuals. To achieve usable and impactful solutions, it is paramount to understand how different content creation and display methods contribute to the work effort of each member (remote and on-site). Additionally, explore how various task scenarios influence the collaborative process, specifically examining how activities with unique characteristics and complexities affect remote work. Hence, the question arises, ’How do different display and interaction methods impact the collaborative process within specific task domains?’ In this paper, two user studies with 30 distinct participants each are described, focusing on different content creation support (Laptop Computer; Video Wall & Keyboard; Interactive Projector) and display methods (Hand-Held Device (HHD); HHD & Articulated Support; Head-Mounted Display (HMD)) for remote and on-site collaborators respectively, during scenarios of remote guidance. Plus, different physical tasks were considered for each study (Lego pieces assembly; Tangram puzzle assembly; Maintenance procedures; Resource management in a map; Training activity in a laboratory classroom), to understand which method stands out according to the characteristics of the said tasks. We report the results obtained, suggesting that for the remote part of the collaborative process, using a Video Wall & Keyboard appears to be more suitable for tasks associated with maintenance procedures, learning, and training activities. As for the on-site part, using HMD was considered the better option for maintenance, and learning tasks, closely followed by HHD & Articulated Support.

Inspection tasks in Industry 4.0 are crucial for ensuring product quality and operational efficiency. Yet human technicians often encounter challenges due to the complexity of modern machinery, the vast amount of data to be processed, and the need for rapid decision-making. These challenges underscore the necessity for innovative solutions that can provide technicians with enhanced data visualization and intuitive guidance. This work proposes a prototype using Pervasive Augmented Reality (AR) and Situated Visualization (SV) to support inspection procedures in industrial settings. This was created in collaboration with industrial partners following a Human-Centered Design (HCD) methodology. This process allowed to identify stakeholders' needs, and challenges, as well as define requirements to guide the development of the prototype. First impressions collected in laboratory environment are described, which helped improve an initial version of the prototype. Subsequently, a user study was conducted at the shop floor environment to evaluate usability, interaction, accuracy and acceptance, as well as collect feedback on how it can be improved to better support such context.

This paper presents an Augmented Reality (AR) application based on Situated Visualization (SV), aimed at increasing awareness of air pollution. Given that technological progress must be achieved through meticulous evaluation processes, the article also presents the assessment of the stated application. Therefore, this article begins by examining how AR applications are appraised. Building upon this review, and focusing particularly on those where SV is provided, a discussion is presented regarding the points that must be carefully considered when evaluating this type of AR application, as well as the factors that need to be measured, along with the most popular methods for evaluating these factors. However, it is noted that if a simultaneous evaluation of all the factors that matter to SV is intended, the number of questions to be asked could be significant. Therefore, a preliminary approach to enable such an appraisal is proposed and used in the mentioned application to demonstrate the merits of the proposed Questionnaire.

The use of Pervasive Augmented Reality (AR) in industrial settings, particularly in the context of Industry 4.0, is a growing area of research. Industry 4.0 is characterized by the integration of advanced technologies, including AR, to enable smart, connected, and autonomous systems in manufacturing and other industries. This study focuses on two systems that use Pervasive AR for logistics and data monitoring on assembly lines. Such systems were designed using cooperative Human-Centered Design (HCD) methodologies in collaboration with partners from the industry sector. We report lessons learned and guidelines on the development of AR systems for industrial environments. In addition, a user study based on a data monitoring laboratory realistic procedure with 8 participants is described. The study compares the usability and effectiveness of a dashboard on Pervasive AR and a web tablet. Overall, results emphasize that AR was better suited when requiring spatial awareness and interventions, while traditional web tablet was better for tasks that require more interaction with the content and faster data monitoring. These findings had implications for the design of industrial tools, such as prototype improvements and idealize new methods, which can guide future efforts to develop and deploy AR applications.

Augmented Reality (AR) is a key technology in the transition to Industry 4.0 and smart manufacturing, gaining reputation in a wide range of industrial fields One promising application is in another field of Industry 4.0, the data monitoring, where AR can be used to visualize and interact with complex and big data in real time, potentially improving the efficiency and accuracy of decision-making In this work, we propose a Pervasive AR tool for data monitoring, developed in collaboration with industry partners. An web application of the same data monitoring function is also created for comparison purposes A Human-Centered Design (HCD) methodology was used to identify the needs and requirements of industrial analysts, which led to the development of these systems Preliminary user studies, with 17 participants having distinct levels of expertise in industry, data analysis and computer science, were conducted to collect opinions and suggestions for improvements, as well as, quantitative data regarding the technologies considered A succeeding user study was then prepared, in which 12 participants used two conditions (C1 - Hands-free Pervasive AR tool for HMDs and C2 - Web tool for tablet devices) to fulfill a set of data monitoring tasks in an industry assembly line The result of these studies confirm the potential of Pervasive AR for data monitoring, as it engages users, promote environmental awareness, contextualizes data and allows fast self-localization.

In recent years, the market has seen the emergence of numerous affordable sensors, interaction devices, and displays, which have greatly facilitated the adoption of Virtual and Augmented Reality (VR/AR) across various applications. However, developing these applications requires a solid understanding of the field and specific technical skills, which are often lacking in current Computer Science and Engineering education programs. This work details an extended version from a Eurographics 2024 Education Paper, reporting a post-graduate-level course that has been taught for the past ten years to almost 200 students, across several Master’s programs. The course introduces students to the fundamental principles, methods, and tools of VR/AR. Its primary objective is to equip students with the knowledge necessary to understand, create, implement, and evaluate applications using these technologies. The paper provides insights into the course structure, key topics covered, assessment methods, as well as the devices and infrastructure utilized. It also includes an overview of various practical projects completed over the years. Among other reflections, we discuss the challenges of teaching this course, particularly due to the rapid evolution of the field, which necessitates constant updates to the curriculum. Finally, future perspectives for the course are outlined. • Description of main features of a VR/AR course attended by almost two hundred students. • illustration of course structure, key topics, assessment methods, devices and infrastructure utilized. • overview of various practical VR/AR projects completed over the years. • reflection on challenges of the past ten years of teaching the VR/AR course. • presentation of future perspectives for a VR/AR course.

Augmented Reality (AR) has the ability to display and interact with additional information aligned with the real world and to support context-driven visualization techniques. Situated Visualization (SV) is a method to represent data in a context that encompasses all the visualizations that change their appearance based on context. Therefore, since AR is typically used so that the user can visualize extra information regarding the surrounding real world environment, this technology is well suited for SV and presents high potential in many situations, as are the cases of collaborative/assistive tasks in the maintenance area and supporting decision-making. AR-based visualizations are commonly constrained to the users' unique egocentric view-point, which reduces their ability to explore all the richness of available information. This limitation is particularly evident in situations that require overviews of all information inside and out of the user's field of view (FOV). This article proposes multi-perspective renderings, transitional interfaces, and the use of multimodal information as methods to extend the user's egocentric viewpoint. The main idea is to coherently present and augment, in the same user's display, dynamic and static feeds of several real and virtual egocentric viewpoints, related to and relevant for the user's task.

This work proposed the use of 3D Reconstruction, Virtual Reality (VR), and a Large Display to enhance the education process of geosciences at the university level. This approach enables teachers to immerse themselves and interact with 3D virtual replicas of real-world locations. In turn, this is cast to a projector, allowing them to better explain specific concepts to an audience of students, which can acquire new knowledge and gain a deeper understanding. A user study was conducted with 20 participants (from geosciences and computer science engineering) to evaluate this proposal versus a traditional static image approach. We report the results obtained, suggesting the VR condition represents the predominant condition for a set of dimensions considered relevant for the tasks defined.

The evolution and miniaturization of technology allows the development of portable solutions for physiology assessment, and the chance to mimic real world settings using Virtual Reality (VR). This work proposes an end-to-end framework for assisting Exposure Therapy (ET) with VR serious games and physiology monitoring. It relies on a generic architecture changing the VR stimuli, i.e., the phobia being considered, while the remaining modules can be re-used for data collection and analysis by a therapist team. It also integrates customizable bio-feedback, triggering specific events or adjusting the amount of stimuli based on the physiological response. The VR serious games were assessed with 16 participants before being integrated into the architecture. The framework’s capacity to acquire bio-signals and other metrics in a synchronous manner was evaluated in two user studies with a total of 23 participants.

This work describes an Augmented Reality Serious Game (ARSG) focused on facilitating the process of acquiring new skills in individuals with motor disabilities. In particular, this game aims to help them control a robotic wheelchair. A racing track was used as a game narrative, including restriction areas, static and dynamic objects, obstacles and various signs. A user study with 20 participants was conducted to compare different methods used to place virtual content on the real-world environment while the user interacts with the game and control the wheelchair in the physical space: C1 - motion tracking using cloud anchors; C2 - offline motion tracking. Results suggest condition C1 is more precise and robust, while condition C2 seems to be easier to configure.

Most efforts on Augmented Reality (AR) remote collaboration have been devoted to on-site individuals in need of assistance. Yet, since remote experts do not have access to the local environment, it is also important to explore how different methods affect the situation understanding and interaction. This work describes a user study with 16 participants aimed at comparing how remote individuals interact with standard (C1) and large-scale displays (C2) while providing assistance during a remote maintenance scenario. Results suggest condition C2 was preferred by the majority of participants, being considered more useful to analyze important details of the task context, as well as more easily create content.

Situated Visualization (SV), encompassing all the visualizations that change their appearance based on context, by considering the visualizations that are relevant to the physical context in which they are displayed [1], has been recognized as a method with potential in many situations, as is the case of supporting decision making [2]. Augmented and Mixed Reality (AR/MR) are well suited to assist in such scenarios, given its ability to display additional data regarding the real-world context and be supported by context-driven visualization techniques [3]. Though some perspectives on the SV model have been proposed, such as space, time, place, activity and community, an appropriate systematization, covering the main definitions and perspectives has yet to be established. Hence, there is an urge to obtain a more comprehensive description. The work presented in this paper characterizes the SV model, within the scope of AR/MR, shows a critical analysis of the existing knowledge, expanding the SV model and in turn hoping to elicit discussion within the research community.

Pervasive Augmented Reality (AR) has been explored to provide experiences that are continuous in space and time, while incorporating layers of contextual information. While most research efforts have been devoted to explore serious games, museums and smart environments, we argue that another interesting area of application is assistive production, in particular training novel workers by displaying instructions throughout the shop floor on what to do. This work describes a user study with 12 participants, aimed at comparing the use of three distinct conditions: C1 - Head-Mounted Display (HMD), C2 - Handheld Device (HHD) and C3 - Paper manual during a set of realistic training tasks, selected based on a collaboration with partners from the industry sector. Results suggest condition C1 was preferred by the majority of participants, being considered more useful to support the worker’s task as it is seems more efficient, hands-free and has a better tracking technology.

Although Mixed Reality (MR) has been explored to support scenarios of remote collaboration, the majority of studies reported in literature have focused on synchronous use-cases, where all team-members can interact in real-time. Hence, an opportunity exist to explore asynchronous situations, in which collaborative actions take place at different times. Despite the physical distance and different time zones, remote experts can generate MR-instructions to assist with emerging problems, suggesting to on-site collaborators where to act, and what to do. This work explored three distinct notification methods for such scenarios: C1 - visual cue; C2 - audio cue; C3 - tactile cue, aiming to comprehend which one captures the attention of on-site team-members easily. A user study with 12 participants was conducted to evaluate these conditions during an asynchronous scenario of remote maintenance. We report participants insights, suggesting tactile cues represent the predominant condition given the scenario context.

To support the nuances of collaborative work, many researchers have been exploring the field of Augmented Reality (AR), aiming to assist in co-located or remote scenarios. Solutions using AR allow taking advantage from seamless integration of virtual objects and real-world objects, thus providing collaborators with a shared understanding or common ground environment. However, most of the research efforts, so far, have been devoted to experiment with technology and mature methods to support its design and development. Therefore, it is now time to understand where the field stands and how well can it address collaborative work with AR, to better characterize and evaluate the collaboration process. In this article, we perform an analysis of the different dimensions that should be taken into account when analysing the contributions of AR to the collaborative work effort. Then, we bring these dimensions forward into a conceptual framework and propose an extended human-centered taxonomy for the categorization of the main features of Collaborative AR. Our goal is to foster harmonization of perspectives for the field, which may help create a common ground for systematization and discussion. We hope to influence and improve how research in this field is reported by providing a structured list of the defining characteristics. Finally, some examples of the use of the taxonomy are presented to show how it can serve to gather information for characterizing AR-supported collaborative work, and illustrate its potential as the grounds to elicit further studies.

Augmented Reality (AR) has been explored with the objective to assist in scenarios of co-located or remote collaboration. To help understand how well collaborative work can be addressed with AR, it is important to foster harmonization of perspectives and create a common ground for systematization and discussion. In this vein, understand relationships among existing dimensions of collaboration, as well as identify research opportunities, is of paramount importance and thus tools that allow visually exploring information associated with Collaborative AR may be most valuable. In this paper, we present a first effort towards the creation of such an interactive visualization tool for exploration and analysis of collaborative AR research. It allows visualize data of selected papers organized according to a human-centered taxonomy on collaborative AR. In order to get insights into whether the structure was understood and if the representation was clear and efficient to use, we evaluated the proposed tool through a user study with 40 participants. Results suggest the tool has potential towards the creation of a shared understanding and identification of existing patterns, trends and opportunities within the field of collaborative AR.

In many situations, performing maintenance procedures can be facilitated by providing on-site professionals with the possibility to request assistance from remote experts, thus performing the task collaboratively. Since remote experts don't have access to the local environment, they need additional mechanisms to provide instructions and guidance in such context. In this ongoing work, we present a Mixed Reality (MR) platform that explores the use of a shared model approach for collaboration scenarios. The platform uses predefined 3D models, which can be accessed by remote experts using Virtual Reality (VR) and by on-site users through Augmented Reality (AR). In addition, the remote expert can manipulate virtual models using different interaction methods and devices, as a mouse and a keyboard (computer), an interactive projector (touch) and controllers (Head Mounted Display). Our goal was to enrich his experience and improve collaborative awareness. We conducted a preliminary user study to evaluate collaboration, and different interaction methods, using as case study the assembly of Lego blocks.

Artificial Intelligent (AI) techniques, such as ma-chine learning (ML), have been making significant progress over the past decade. Many systems have been applied in sensitive tasks involving critical infrastructures which affect human well-being or health. Before deploying an AI system, it is necessary to validate its behavior and guarantee that it will continue to perform as expected when deployed in a real-world environment. For this reason, it is important to comprehend specific aspects of such systems. For example, understanding how neural networks produce final predictions remains a fundamental challenge. Existing work on interpreting neural network predictions for images via feature visualization often focuses on explaining predictions for neurons of one single convolutional layer. Not presenting a global perspective over the features learned by the model leads the user to miss the bigger picture. In this work we focus on providing a representation based on the structure of deep neural networks. It presents a visualization able to give the user a global perspective over the feature maps of a convolutional neural network (CNN) in a single image, revealing potential problems of the learning representations present in the network feature maps.

The FICA project - Tools for Identifying and Combating Dropout - started at the University of Aveiro in 2015 with the aim to help reduce and prevent dropouts and increase academic success among university students. Within the project a signicant amount of data is provided to different University stakeholders to monitor academic issues, however, these data are currently provided in large tables, a format difficult to analyze. In this paper, we present the main aspects of the data, the users and contexts of use. We also propose an approach to allow the visual and interactive exploration of the FICA project data to help monitor the path of the students and identify risk indicators and failure factors that can lead to critical situations such as dropout. A solution developed using the participatory design methodology is presented, detailing all stages of its creation process, from the requirements elicitation based on focus groups and interviews, design and prototype development in Power BI to its evaluation. Some suggestions for future work are also presented.

In image segmentation, there is often more than one plausible solution for a given input. In medical imaging, for example, experts will often disagree about the exact location of object boundaries. Estimating this inherent uncertainty and predicting multiple plausible hypotheses is of great interest in many applications, yet this ability is lacking in most current deep learning methods. In this paper, we introduce stochastic segmentation networks (SSNs), an efficient probabilistic method for modelling aleatoric uncertainty with any image segmentation network architecture. In contrast to approaches that produce pixel-wise estimates, SSNs model joint distributions over entire label maps and thus can generate multiple spatially coherent hypotheses for a single image. By using a low-rank multivariate normal distribution over the logit space to model the probability of the label map given the image, we obtain a spatially consistent probability distribution that can be efficiently computed by a neural network without any changes to the underlying architecture. We tested our method on the segmentation of real-world medical data, including lung nodules in 2D CT and brain tumours in 3D multimodal MRI scans. SSNs outperform state-of-the-art for modelling correlated uncertainty in ambiguous images while being much simpler, more flexible, and more efficient.

Assembly procedures are a common task in several domains of application. Augmented Reality (AR) has been considered as having great potential in assisting users while performing such tasks. However, poor interaction design and lack of studies, often results in complex and hard to use AR systems. This paper considers three different interaction methods for assembly procedures (Touch gestures in a mobile device; Mobile Device movements; 3D Controllers and See-through HMD). It also describes a controlled experiment aimed at comparing acceptance and usability between these methods in an assembly task using Lego blocks. The main conclusions are that participants were faster using the 3D controllers and Video see-through HMD. Participants also preferred the HMD condition, even though some reported light symptoms of nausea, sickness and/or disorientation, probably due to limited resolution of the HMD cameras used in the video see-through setting and some latency issues. In addition, although some research claims that manipulation of virtual objects with movements of the mobile device can be considered as natural, this condition was the least preferred by the participants.

The decision-making process and the development of decision support systems (DSS) have been enhanced by a variety of methods originated from information science, cognitive psychology and artificial intelligence over the past years. Situated visualization (SV) is a method to present data representations in context. Its main characteristic is to display data representations near the data referent. As augmented reality (AR) is becoming more mature, affordable and widespread, using it as a tool for SV becomes feasible in several situations. In addition, it may provide a positive contribution to more effective and efficient decision-making, as the users have contextual, relevant and appropriate information to endorse their choices. As new challenges and opportunities arise, it is important to understand the relevance of intertwining these fields. Based on a literature analysis, this paper addresses and discusses current areas of application, benefits, challenges and opportunities of using SV through AR to visualize data in context and to support a decision-making process and its importance in future DSS.