Search Results (302)

Scientific visualization is changing from passive observation to active, AI-assisted collaboration. While Extended Reality (XR) has proven valuable for comprehending dense 3D arrays, traditional VR applications are typically deployed in rigid, single-purpose, and monolithic architectures. In this paper, we present the evolution of ASCRIBE-XR: a virtual reality platform backed by remote computation that has been re-engineered into a dynamic, service-oriented ecosystem. We introduce three core innovations that make immersive data analysis easier, faster, and more flexible when using multimodal scientific imaging. First, a lightweight Python REST interface decouples XR logic from the rendering engine, enabling real-time, programmable scene customization and on-demand data generation. Second, we present a Specimen Catalog architecture that lets the platform pivot between radically different disciplines, ranging from archaeological heterogeneous concrete and fuel-cell membranes to the root system of a bioenergy grass, by describing each dataset through portable metadata rather than hard-coded application logic. Finally, we introduce a prompt-driven layer powered by the Claude Agent SDK, allowing researchers to generate, segment, and manipulate volumetric and mesh data through natural language dialogue within the virtual space. For example, applying foundation models such as the Segment Anything Model (SAM) to perform zero-shot segmentation on demand. By bridging human intent with remote computation, ASCRIBE-XR relaxes the constraints of conventional visualization tools, offering a highly adaptable, conversational platform for scientific discovery with human auditing. Full article

Interpreting nonlinear seismic structural analysis results in nuclear power plants remains challenging when conventional post-processing tools are used, as these require analysts to reconstruct structural meaning from fragmented 2D or non-immersive 3D views. This limits spatial understanding, weakens traceability between global response and local damage mechanisms, and constrains the communication of findings in critical infrastructure contexts. In response, this paper proposes a human-centered methodological framework for integrating BIM models and nonlinear seismic simulation results into an immersive virtual reality environment for structural interpretation and risk-free inspection in nuclear power plants. The proposed workflow connects structural seismic analysis, result post-processing, the reference BIM model, and its deployment in a VR environment developed in Unreal Engine. The framework was implemented through a case study based on a generic nuclear power plant, resulting in a functional demonstrator. A qualitative evaluation based on an expert walkthrough showed the potential of the proposed workflow to enhance spatial understanding, simplify comparison between structural states, enable risk-free inspection environments, and facilitate technical communication. The main contribution of the study is to demonstrate how an integrated virtual reality environment can act as a complementary, human-centered interpretive interface that reduces cognitive fragmentation in conventional structural analysis workflows. Full article

The increasing complexity of healthcare data ecosystems demands educational technologies capable of supporting data-intensive learning through advanced analytics, immersive interfaces, and learning analytics. This paper presents a cloud-based reference architecture and a prospective evaluation protocol for integrating Business Intelligence (BI), Extended Reality (XR), and learning analytics in health data science education. The proposed architecture is informed by a systematic literature review conducted according to the PRISMA 2020 guidelines, which screened 613 records retrieved from four databases and retained 56 studies for qualitative synthesis. The review indicates that, although BI and XR technologies have independently been associated with educational benefits, empirical evidence supporting integrated educational architectures combining BI, XR, and learning analytics remains limited, particularly in health data science education. Based on these findings, the paper specifies a layered reference architecture comprising a cloud analytics engine, an immersive visualization engine, an interoperability layer, and a learning analytics pipeline designed to support adaptive and AI-assisted educational services during subsequent implementation phases. The reference architecture is partially instantiated within the curricular unit Health Data Analysis and Visualization of the Digital Health programme at the Polytechnic University of Porto, where the BI and XR components are currently deployed and used within the course, while the interoperability middleware, learning analytics infrastructure, and AI-assisted services remain under development or are specified as architectural capabilities. To support future empirical validation, the paper also defines a comprehensive prospective evaluation protocol comprising predefined outcomes, established instruments with published psychometric properties, together with an expert-developed health data literacy assessment undergoing content validation, research hypotheses, power analysis, a statistical analysis plan, and ethical and data-governance provisions. The manuscript makes four principal research contributions: (i) a cloud-based reference architecture for BI–XR integration, (ii) a computational learning analytics pipeline specification, (iii) an interoperable system design for health data science education, and (iv) a prospective evaluation protocol to guide the future validation of the proposed reference architecture. Full article

Background: Subjective experience is increasingly recognised as an important component of brain–computer interface (BCI) performance in extended reality (XR) environments. Although embodiment and simulator sickness are known to influence user experience, their relationships with cortical activity during XR–BCI operation remain poorly understood. Building upon our previous investigations of embodiment and simulator sickness in XR–BCIs, the present study examined whether these subjective dimensions are associated with distinct neurophysiological patterns during repeated XR–BCI use in a participant with chronic spinal cord injury (SCI). Methods: Seventeen XR–BCI sessions performed by a participant with chronic complete SCI were analysed. Bayesian correlation analyses examined associations among embodiment, simulator sickness, BCI performance, and EEG activity. Multiple linear regression was used to identify variables independently associated with sensorimotor beta activity, and the robustness of the regression findings was evaluated using bootstrap estimation and leave-one-out sensitivity analyses. Results: Bayesian analyses identified two principal patterns of association. Sense of embodiment was positively associated with frontal theta activity (F3), whereas simulator sickness showed a negative association with sensorimotor beta activity (C3–C4). As expected, classifier acquisition accuracy was strongly associated with subsequent BCI performance. Multiple regression demonstrated that simulator sickness was the only variable independently associated with C3–C4 beta activity after accounting for embodiment and BCI performance. This association remained robust following bootstrap estimation and leave-one-out sensitivity analyses. Conclusions: Although limited to a single participant, these findings suggest that different dimensions of subjective experience during XR–BCI operation are associated with partially distinct neurophysiological correlates. In particular, simulator sickness was the variable most consistently associated with sensorimotor beta activity across all analyses. These findings provide a foundation for future longitudinal investigations of the neural mechanisms linking subjective experience and cortical dynamics during XR–BCI use. Full article

Recent technological advancements have significantly transformed the diagnosis and management of cardiovascular disease. Traditional reliance on two-dimensional (2D) and three-dimensional (3D) imaging has been enhanced by emerging 3D visualization technologies, particularly 3D printing and extended reality (XR). Three-dimensional printing enables the creation of patient-specific physical models that accurately replicate cardiovascular anatomy and pathology. These models play a crucial role in surgical planning, simulation of interventional procedures, medical education, and patient communication, offering tangible insights into complex cardiovascular structures. XR, on the other hand, provides an immersive 3D environment for exploring volumetric imaging data under interaction with the physical world. This enhances the understanding of intricate cardiovascular anatomy and pathology, supporting more informed clinical decision making and pre-surgical planning. This review summarizes the current applications of 3D printing and XR in cardiovascular surgery planning and intervention, emphasizing their potential to address challenges in planning complex procedures. Limitations and future directions for research and clinical integration are also discussed. Full article

Digital technologies have significantly advanced documentation, management, and dissemination of Cultural Heritage (CH) through reality capture, Heritage Building Information Modelling (HBIM), and Extended Reality (XR). However, the digitisation of large and historically stratified heritage sites still relies on fragmented workflows that compromise interoperability and interrupt the continuity of geometric and semantic information throughout the heritage lifecycle. This paper proposes and validates a platform-independent, ecosystem-based methodology integrating multi-scale reality capture, hybrid Scan-to-HBIM modelling, semantic information management, interoperability, and XR within a continuous digital workflow. The methodology was validated through the large-scale digitisation of the Certosa di Pavia, where more than 1500 terrestrial laser scans, over 200,000 photographs, and approximately 50 billion points were acquired across a monumental complex covering nearly 331,000 m². The proposed framework reconstructs irregular architectural geometries with millimetre-scale accuracy (σ = 0.005 m) while preserving geometric reliability, semantic consistency, and information traceability across point-cloud processing, HBIM environments, Common Data Environments (CDEs), and XR applications. The results demonstrate that the effective digitisation of complex CH depends not only on the accuracy of individual technologies but also on their coordinated integration within interoperable digital ecosystems. The proposed methodology provides a transferable framework for preserving knowledge continuity throughout the heritage lifecycle, enabling HBIM to evolve from a geometric representation into a dynamic knowledge environment supporting conservation, management, research, education, and public dissemination. Full article

This paper addresses steady-state visually evoked potential (SSVEP) classification in wearable extended-reality (XR) brain–computer interfaces (BCIs), with a threefold objective. First, it investigates the effectiveness of deep learning (DL)-based SSVEP classification under XR stimulation, where platform-dependent rendering, optical see-through visualization, reduced luminance contrast, and interaction with the real environment may degrade the quality of the elicited EEG response. Second, a metrology-based performance assessment is proposed according to the Guide to the Expression of Uncertainty in Measurement (GUM), with classification accuracy and information transfer rate (ITR) expressed as best estimates with associated standard uncertainties. Finally, EEG channel reduction is analyzed toward lightweight XR-SSVEP implementations. As a representative SSVEP-specific DL model, the SSVEP time-frequency fusion network (SSVEP-TFFNet) is evaluated on an open XR benchmark dataset comprising 30 subjects and 1200 trials acquired using Microsoft HoloLens 2. A subject-independent comparison with filter-bank canonical correlation analysis (FBCCA) is performed, while intra- and inter-subject variability are incorporated into the uncertainty evaluation. Results show that SSVEP-TFFNet outperforms FBCCA under the considered XR conditions. Moreover, reduced 6- and 4-channel configurations preserve performance close to the full 8-channel montage. These findings provide evidence of the potential of suitably selected DL models for XR-based SSVEP classification and support uncertainty-aware, reduced-electrode wearable implementations. Full article

Background: Extended reality (XR) technologies, including augmented reality (AR), mixed reality (MR), and virtual reality (VR), are increasingly used in craniomaxillofacial (CMF) surgery; yet, no study has systematically mapped the global development of this research. This study provides the first comprehensive evidence mapping of XR and related computational technologies in CMF surgery, identifying research trends, collaborative networks, and future directions. Methods: A systematic search of the Web of Science (WoS) Core Collection (data extracted April 2025) combined XR terms (AR, MR, VR, image-guided surgery, and computer-assisted surgery) with CMF surgery terminology. After applying the inclusion criteria, 778 English-language articles published between 1988 and 2024 were analyzed using performance analysis and science mapping tools, including HistCite and VOSviewer. Results: Publication output grew exponentially, accelerating markedly after 2010. The Journal of Cranio-Maxillofacial Surgery led in both volume and internal citation impact. The USA, Germany, and China accounted for most of the output, with Germany showing a disproportionately high internal citation impact relative to its publication volume. Three developmental phases were identified: foundational computer-assisted surgery (1990s–2000s), technological development and validation (2000s–2010s), and clinical application optimization centered on AR and VR (2010s–present). Co-authorship networks revealed strong regional clusters with limited cross-cluster connectivity. No publications were identified from African countries other than Egypt. Conclusions: XR research in CMF surgery has grown substantially over 36 years, with a clear shift toward AR and VR applications. Geographic concentration limited international collaboration, and a focus on technical rather than patient-centered outcomes remains a key challenge. Future work should prioritize global participation, comparative clinical evidence, and patient-reported outcomes to support wider surgical adoption. Full article

Extended reality (XR) platforms may support cognitive and functional interventions in mild cognitive impairment (MCI), but their clinical feasibility depends on perceived usability, comfort, and user acceptance. This exploratory pilot study examined subjective acceptability of three integrated XR device–task configurations: a non-immersive Touch TV configuration, a semi-immersive Cave Automatic Virtual Environment (CAVE) configuration, and a fully immersive head-mounted display (HMD) configuration. The study included 21 individuals with MCI and 10 cognitively healthy controls. Participants experienced all three device–task configurations in randomized order and completed the Digital Immersion Satisfaction Questionnaire (Qu.G.I.D.), an ad hoc instrument assessing digital literacy, perceived ease of use, preference, and willingness to use the configurations for future applications. Perceived ease of use scores were generally high and did not differ significantly between groups. Repeated-measures exploratory analyses showed no statistically robust configuration effect for perceived ease of use or preference scores. CAVE was numerically the most frequently selected device–task configuration for future applications, but this difference was not statistically significant when non-mutually exclusive selections were analyzed using Cochran’s Q. Digital literacy was not positively associated with Touch TV preference. Because device type, level of immersion, interaction modality, and task content were not experimentally separable, these findings cannot determine whether ratings reflected hardware characteristics or the specific activities performed. The results should therefore be interpreted as preliminary descriptive self-report acceptability and acceptability data, not as evidence of comparative device–task configurations effectiveness. Larger, balanced, counterbalanced studies using validated usability scales, objective performance metrics, interaction-quality measures, and cybersickness assessment are required before hypotheses for future controlled studies can be formulated. Full article

Artificial intelligence (AI)-based 3D eye tracking is a fundamental enabling technology for human–computer interaction (HCI), extended reality (XR), and intelligent spatial computing. However, physical evaluation methodologies are heavily limited by non-repeatable human micro-movements, the lack of precise millimeter-level ground truth, and systemic camera calibration errors. Addressing these limitations, this paper delivers a methodological meta-contribution to the field by presenting a high-fidelity facial digital twin benchmark framework using NVIDIA Isaac Sim for the rigorous and repeatable evaluation of 3D eye-tracking algorithms. Rather than focusing on incremental algorithmic modifications, our framework establishes a standardized, hardware-free testing paradigm. By systematically sampling virtual facial poses under identical rendering configurations, it generates dense, fully repeatable trajectories with mathematically exact spatial ground truth across landmark-based, 3DMM-based, and direct regression architectures. To isolate intrinsic algorithmic capabilities from extrinsic calibration biases, we propose a self-referenced relative-motion evaluation protocol operating in a facial-centered local reference frame. Comprehensive diagnostics are performed across multiple key dimensions, including localization accuracy, temporal jitter, detection robustness, and pose sensitivity, culminating in a newly introduced Comprehensive Performance Index (CPI) to aggregate these multi-dimensional metrics. Statistical hypothesis testing reveals consistent performance hierarchies: landmark and 3DMM methods achieve superior geometric consistency and temporal stability by leveraging parametric shape constraints, whereas direct regression models exhibit severe tracking degradation and failure under extreme rotations. By resolving the long-standing benchmark replication bottleneck, this extensible digital twin platform establishes a standardized, reproducible methodology for developing and certifying trustworthy human-centric perception systems. Full article

Three-dimensional scanning, additive manufacturing, and extended reality are now widely used in digital heritage, but they are often discussed as separate technical tools rather than as parts of a user-facing workflow. This review approaches them from the standpoint of user experience, asking how digital capture, model preparation, physical replication, immersive interpretation, and hybrid access affect the way heritage content is understood, used, and trusted. The paper develops a critical narrative discussion of recent work and design practices, without adopting a systematic review protocol. Particular attention is given to educational use, museum interpretation, accessibility, tactile interaction, XR navigation, perceived authenticity, and the evaluation of user experience. The discussion indicates that UX is shaped before the final interface appears: incomplete capture, opaque model editing, poorly readable replicas, or confusing XR layers can all weaken the cultural value of an otherwise advanced system. Recurring barriers include technical complexity, interoperability problems, limited staff training, institutional constraints, accessibility gaps, and uncertainty around automated reconstruction. The review argues that automated digital heritage workflows should be planned as human-centered systems, where efficiency is balanced with usability, inclusion, transparent interpretation, and user-based evaluation. Full article

Extended Reality (XR), including Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), is increasingly used to support experiential learning in Initial Teacher Education (ITE). This systematic review aimed to examine how XR technologies are integrated into university-based ITE programmes and their reported educational outcomes. Following PRISMA 2020 guidelines, a multi-source search was conducted across major databases (e.g., Scopus, Web of Science) and the grey literature (last search: January 2026). Eligible studies included empirical research on XR in ITE published between 2016 and 2026; non-empirical and non-ITE studies were excluded. Risk of bias was assessed using established appraisal criteria, and results were synthesised using a narrative thematic approach. A total of 32 studies were included. Findings indicate that XR is primarily used for classroom management training, microteaching, and reflective practice. Across studies, immersive simulations were associated with improvements in teacher self-efficacy, classroom management skills, and reflective decision-making. However, accessibility and inclusion strategies remain underdeveloped, and evidence of transfer to real classroom practice is still limited. Overall, XR functions most effectively as a preparatory tool that complements practicum-based training. Full article

The shift toward hybrid intelligence in learning systems emphasizes the integration of human and AI cognitive capabilities into unified problem-solving processes. Yet, design principles for enabling such systems in immersive learning environments remain insufficiently understood. Immersive learning environments, realized through extended reality (XR), introduce unique affordances and challenges for embodied interaction, spatial communication, and co-presence that demand rethinking how collaboration unfolds. This conceptual paper proposes a framework and a set of design commitments for enabling sensible Human–AI collaboration for hybrid intelligence in immersive learning environments. Drawing on research and theories in Human–AI teaming and Human–AI collaboration, XR interaction design, learning sciences, and cognitive ergonomics, we identified four key dimensions of collaboration: collaborative agency and role distribution, shared attention and regulation, embodied and spatial interaction, and mutual intelligibility and adaptive support. We outline a conceptual framework describing how humans and AI can jointly achieve goals, negotiate roles, coordinate attention, and engage in knowledge co-construction within immersive learning spaces for hybrid intelligence. We further argue that immersive contexts require new forms of mutual intelligibility, spatial communication, and adaptive support to enable hybrid intelligence characterized by adaptive co-intelligence that improves learning processes in real time. Further, we advance a definition of hybrid intelligence specific to immersive learning that identifies three emergent properties: collaborative fluency, adaptive co-presence, and distributed knowledge growth. The paper closes with implications for researchers and practitioners and identifies constitutive design tensions that future work would navigate. Finally, this paper is conceptual in nature; the framework presented is offered as a theoretically grounded hypothesis for future empirical inquiry. Future research directions include validating the emergent properties through observational and experimental studies in actual XR environments and developing measurement tools adequate to the spatial, embodied, and real-time dimensions the framework identified. Full article

Everyday heritage objects are often overlooked despite their cultural significance and vulnerability to surface degradation caused by environmental exposure, material ageing, and human interaction. This review examines how surface characterization, digital documentation, additive manufacturing, and extended reality (XR) technologies can be integrated to support the conservation, replication, and inclusive dissemination of such assets. The study synthesizes recent advances in non-destructive surface analysis methods, including spectroscopic and imaging techniques, alongside 3D scanning approaches capable of capturing both geometry and surface condition. These data are linked to additive manufacturing workflows for producing accurate and durable replicas, with particular attention to surface fidelity and material selection. The review further explores how tactile replicas and multimodal interpretation strategies can enhance accessibility for visually impaired users, addressing limitations of visually dominant heritage practices. XR technologies are discussed as complementary tools for interpretation and remote access. The findings highlight that combining surface-focused conservation with digital and fabrication technologies enables more resilient, accessible, and sustainable heritage management. Future research should focus on standardizing inclusive design approaches and improving the integration of surface data into digital and physical reproduction pipelines. Full article