Search Results (305)

Accurate distance measurement in outdoor environments remains a challenging problem for mobile augmented reality (AR) systems due to sensor noise, environmental variability, and the limitations of single-modality approaches. Existing consumer AR solutions often prioritize usability over metric robustness, leading to performance degradation in large-scale or heterogeneous outdoor scenarios. This work presents EfMAR, an adaptive framework for outdoor mobile AR-based geospatial measurements that integrates multiple sensing modalities through a structured sensor fusion architecture. EfMAR combines visual SLAM, inertial sensing, depth information, and global positioning cues to improve robustness and consistency in distance estimation across diverse outdoor conditions. Beyond implementation, the framework formalizes a reusable architectural model for adaptive multi-sensor fusion, supporting reproducibility and future comparative research. A dedicated dataset is described, comprising 584 unique real-world evaluation instances collected across representative outdoor scenarios. External literature-derived data were utilized strictly as calibration baselines for modeled operational degradation profiles, maintaining methodological transparency. Performance evaluation focuses on analyzing relative behavior, stability, and variability across sensing approaches rather than establishing absolute accuracy benchmarks. Comparative results across multiple distance ranges and environments indicate that hybrid sensor fusion strategies exhibit more stable and consistent performance trends compared to single-modality solutions, particularly in challenging urban contexts. Dispersion analysis further highlights the influence of environmental factors such as lighting conditions and spatial scale on measurement variability. Overall, the results position EfMAR as a flexible and adaptive framework designed to enhance robustness in outdoor AR-based geospatial measurement tasks. By emphasizing consistency, transparency, and architectural generalization, this work contributes a practical foundation for future research and development in mobile AR sensing for real-world outdoor applications. Full article

Traditional evaluations of artificial intelligence (AI) systems in the dynamic, operator-dependent, and time-sensitive field of interventional radiology (IR), focusing solely on algorithmic performance, often fail to capture their real-world clinical impact. This narrative review aims to provide an overview of the current state of the art of AI integration in IR through human–AI interaction (HAI), while offering a critical perspective on their clinical integration, limitations, and future directions. A comprehensive survey of recent literature was performed, focusing on AI applications across procedural phases. The review emphasizes systems providing decision support, real-time procedural verification, and immersive interfaces (augmented and virtual reality), while critically evaluating determinants of effective clinical adoption. AI has shown preliminary potential to support operator performance in selected interventional radiology tasks, although most applications remain experimental, retrospective, or evaluated in phantom or preclinical settings. Potential benefits include structuring uncertainty in patient selection and procedural planning, supporting assessment of device positioning and treatment outcomes, and integrating AI-derived outputs into the operator’s spatial field through immersive technologies. The clinical utility of these systems appears to be influenced by human–AI interaction, with interpretability, workflow integration, and trust calibration representing key determinants of effective use beyond algorithmic accuracy alone. The potential value of AI in interventional radiology appears to derive from its integration into human decision-making rather than from standalone predictive performance alone. A human-centered, interaction-based model supports understanding current applications, address challenges, and guide the development of adaptive, real-time systems for dynamic procedural environments. Full article

Mixed-reality (MR) technologies have enhanced anatomy education through immersive three-dimensional visualization; however, most existing systems lack tutoring capabilities that respond contextually during anatomical exploration. This paper presents a reproducible MR anatomy learning platform implemented on the Apple Vision Pro that integrates the open-source Z-Anatomy atlas, with cardiovascular anatomy as the case domain. The system supports interactive exploration through hand gestures and eye tracking, alongside natural-language voice interaction. To provide context-grounded tutoring, we incorporate a retrieval-augmented generation (RAG) voice assistant whose responses are bounded by the Terminologia Anatomica knowledge base and weighted by the learner’s current spatial focus, with spatially anchored labels supporting contextual understanding. The platform was profiled on Apple Vision Pro hardware using Xcode Instruments and exercised through scenario-based walkthroughs of representative anatomical exploration tasks; the system met its real-time interaction and rendering thresholds across eight integrated anatomical systems. By leveraging open-source content and a substitutable AI backend, the architecture reduces software-licensing and development costs by an estimated one to two orders of magnitude relative to comparable proprietary systems and ports across XR platforms via a single bridge layer. Full article

While Unmanned Aerial Systems (UAS) are increasingly used for infrastructure inspection, a critical gap exists between optimized path planning and reliable real-world execution. Fully autonomous flights face regulatory constraints and environmental risks, whereas manual piloting introduces inconsistencies that compromise data quality. To address this gap, this study proposes a human-in-the-loop assistive navigation platform that enables pilots to follow preplanned inspection trajectories while maintaining manual control. The proposed system integrates an Augmented Reality (AR)-based guidance module that provides real-time viewpoint localization with a mesh-coupled quality monitoring module that continuously evaluates view redundancy and triangulation uncertainty. A proof-of-concept field demonstration through an on-site façade inspection example indicates that the proposed platform has the potential to improve the consistency of viewpoint distribution, achieving closer adherence to planned spacing and stand-off distance. This results in more uniform spatial sampling, enhanced view redundancy, and reduced variability in theoretical uncertainty, leading to improved geometric conditions for Structure-from-Motion (SfM) reconstruction. Overall, the field demonstration highlights the potential of combining computational guidance with human decision-making to support reliable and high-quality UAS-based infrastructure inspection. Full article

This paper presents the design and development of an adaptive augmented reality (AR) assistance system for complex mechanical assembly tasks. Integrating a wrist-worn optical heart rate sensor to evaluate the user’s cognitive state, the system is intended to run as a standalone application on the Meta Quest 3 headset. The system displays instructions and visual cues directly overlaid on the user’s physical workspace and constantly monitors their heart rate variability through the sensor as an estimate of their cognitive load. When the system detects an overload, it dynamically adjusts the presentation of information—for example, it slows down pacing, simplifies instructions, or switches to a different interaction modality (audio)—as an attempt to reduce the overload. The paper makes three contributions: first, it provides a documented standalone integration of physiological sensing with adaptive interface logic on a mixed reality headset without external compute infrastructure; second, it provides a systematic characterization of platform-specific tracking incompatibilities on the Meta Quest 3, documenting the progression through four spatial registration strategies and the specific failure condition that triggered each transition; third, it reports spatial interface design observations from iterative developer testing in the current prototype configuration, including panel height ranges not previously reported in the AR interface literature at this level of specificity. The paper also discusses the within-subjects evaluation protocol that is planned for final system testing with actual users. The work is intended as an engineering and design contribution that establishes the foundation for subsequent empirical evaluation of adaptive AR guidance in industrial assembly contexts. Full article

Transitioning toward efficient and digital industrial design requires preliminary tools that support early decision-making in plant layout studies. This qualitative and exploratory study analyzes an Artificial Intelligence (AI)-assisted and Augmented Reality (AR)-supported workflow within the Systematic Layout Planning (SLP) framework for the preliminary spatial evaluation of a cosmetic emulsion production plant. The study was developed as a case study based on a previously reported layout for obtaining cosmetic emulsions from Amazonian oils. A top-view layout was examined through structured prompts aligned with SLP criteria, including product journey, activity relationships, relational diagrams, and space requirements. ChatGPT was used only as a qualitative reasoning assistant, without optimization, prediction, mathematical modeling, or algorithmic functions. After the AI-assisted review, the refined layout was represented in three dimensions and visualized through AR in a real environment. The results identified potential improvements related to operational flow, traceability, critical area relationships, and spatial organization. AR-assisted visualization provided preliminary visual evidence of compatibility between the refined layout and the selected site, supporting an early review of circulation, access, and volumetric behavior. The sequential integration of SLP, AI, and AR is proposed as an exploratory workflow for early-stage layout evaluation, pending future quantitative validation studies and expert technical review. Full article

In safety-critical domains such as aviation, autonomous driving, and defense, operators must process complex spatial and radar data under severe time pressure. Traditional two-dimensional interfaces often force a “head-down” posture, increasing cognitive workload and impairing situational awareness. Extended reality and multimodal interfaces—incorporating gesture, voice, and haptic feedback—offer a promising paradigm to mitigate these limitations by enabling natural, egocentric data visualization. This scoping review systematically maps the empirical evidence on immersive and multimodal interfaces designed for radar and spatial data visualization in critical operational environments. Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines, a systematic search was conducted across five major databases for articles published between 2015 and 2025. Out of 538 unique records screened, 54 studies met the eligibility criteria and underwent structured data charting. The findings reveal a technological ecosystem heavily dominated by augmented reality and virtual reality, supplemented by non-extended reality multimodal baselines (n = 8) to evaluate sensory load distribution. While subjective metrics such as the NASA Task Load Index (n = 17, 31.4%) dominate current evaluation practices, there is a notable scarcity of objective real-time physiological biosensors (n = 7, 13%). Crucially, the synthesized data challenges uncritical technological optimism: while multimodal extended reality effectively mitigates visual bottlenecks, certain modalities like mid-air gestures frequently induce physical fatigue and a documented speed–accuracy trade-off. To fully realize the potential of immersive decision support systems, future research must prioritize standardized, ecologically valid evaluation frameworks and explore artificial intelligence-driven adaptive interfaces capable of dynamically modulating information density based on operator workload. Full article

Over the last decade, augmented reality (AR) has been widely adopted in architectural education, yet it is still often treated as a visualization add-on rather than as an operative design medium. This paper argues that AR becomes pedagogically meaningful when it is anchored to physical or graphic artefacts so that overlays function not as final images, but as reversible instruments for testing, adjustment, and spatial verification. Building on reflection-in-action as a model of situated design learning, the study examines two teaching experiences: one focused on the AR-based translation of complex two-dimensional graphic fields into three-dimensional hypotheses, and another centred on kinematic reasoning through equilibrium and iterative adjustment. The article proposes that error within AR-based workflows has a double pedagogical role: first, as corrective feedback, when mismatch reveals imprecision, insufficient legibility, or unstable alignment in the target; and second, as generative design feedback, when recalibration and reconfiguration trigger new spatial hypotheses or bidirectional transfers between physical and digital models. Evidence is based primarily on analytic observation of documented episodes and on visual documentation of process transformations, complemented by a background evaluative scaffold and supplementary student feedback where available. Results indicate that AR can (a) increase the material and graphic precision of the supporting artefact; (b) strengthen spatial and kinematic understanding by making intermediate states and inconsistencies visible; and (c) turn mismatch and recalibration into operative parts of the design process itself. The paper therefore reframes AR in architectural education not as a representational endpoint, but as a medium of verification, adjustment, and projective transformation. Full article

Fresh engineering students are often required to absorb a large amount of new information within a short period of time, which can be academically and emotionally challenging. To address this challenge, this study introduces AUMOR, a mobile application designed to enhance university orientation by delivering contextual information at the point of need. It integrates GPS-based localization with QR code triggers to provide real-time, location-specific guidance and interactive content through an augmented reality (AR) interface. It uses GPS functionality to provide real-time location-based services, including information about academic buildings, student services, and recreational facilities. The QR codes on devices and laboratory equipment provide relevant information when scanned. A post-deployment user perception survey was conducted using a paper-based questionnaire involving 128 participants, including both students and faculty members. The results indicate that users perceived the application as helpful in enhancing their spatial awareness, navigation confidence, and ability to locate campus facilities, demonstrating high levels of usability and acceptance. The findings suggest that students perceived AUMOR as helpful for university orientation and suggest potential as a scalable solution. Full article

Background/Objectives: Transsphenoidal surgery has become the gold standard for the treatment of sellar and parasellar lesions, but it remains associated with significant anatomical challenges and the risk of intraoperative complications. The limitations of conventional imaging in depicting the complex three-dimensional anatomy of the skull base have led to a growing interest in virtual (VR) and augmented reality (AR) technologies, which offer enhanced spatial visualization, preoperative simulation, and image-guided intraoperative navigation. This systematic review aims to evaluate the current evidence on the role of virtual and augmented reality in transsphenoidal surgical interventions, with a focus on their impact on preoperative planning, intraoperative orientation, surgical outcomes, and neurosurgical training. Methods: A systematic literature search was conducted in accordance with PRISMA 2020 guidelines across PubMed, Scopus, and Web of Science for the period 2015–2025. MeSH terms and free-text keywords related to transsphenoidal surgery, sphenoid sinus anatomy, and VR/AR technologies were combined using Boolean operators. Risk of bias was assessed using RoB 2.0 for RCTs; methodological quality was assessed using the Newcastle–Ottawa Scale for observational studies and AMSTAR 2 for systematic reviews. Clinical, morphometric, and experimental studies evaluating VR/AR applications were included. Data were extracted using a standardized protocol and synthesized through qualitative analysis, with subgroup analysis by technology type (VR vs. AR) and clinical application domain. Results: A total of 218 publications were identified, of which 52 met the inclusion criteria (clinical studies n = 12, simulation and technology studies n = 30, morphological studies n = 10). VR-based three-dimensional reconstructions were consistently associated with improved preoperative spatial orientation and anatomical landmark recognition. AR systems demonstrated a meaningful contribution to intraoperative navigation, with reported reductions in time to target and improved visualization of critical neurovascular structures. VR platforms showed high effectiveness in surgical training, with shorter learning curves and improved technical performance. However, the majority of included studies were small observational cohorts, simulation studies, or expert overviews, with substantial heterogeneity in methodology, technology platforms, and outcome measures, precluding quantitative meta-analysis. Conclusions: Virtual and augmented reality represent clinically promising adjuncts to transsphenoidal surgery, with demonstrated benefits in preoperative planning, intraoperative navigation, and surgical training. These conclusions should be interpreted in the context of a predominantly early-phase and heterogeneous evidence base. Standardized protocols, larger prospective studies, and randomized trials are needed before the integration of VR/AR with navigation systems and artificial intelligence can be established as a routine component of personalized transsphenoidal surgery. Full article

This systematic review examines how Extended Reality (XR) technologies, i.e., Virtual (VR), Augmented (AR), Mixed (MR), and Spatial Augmented Reality (SAR) are designed, implemented, and evaluated in cultural heritage (CH) applications, addressing five research questions: (RQ1) How were XR technologies applied in CH between 2021 and 2025? (RQ2) What interaction paradigms are used, and how do they shape engagement and meaning making? (RQ3) What user experience outcomes are reported in XR CH applications? (RQ4) What evaluation methods are employed and what methodological gaps remain? (RQ5) What challenges persist across XR heritage implementations? Peer-reviewed, English-language studies reporting on implemented XR systems in CH contexts with empirical or evaluative data were included; conceptual articles without a described implementation, non-English publications, and studies published before January 2020 were excluded. Scopus, Web of Science, IEEE Xplore, and the ACM Digital Library were searched for publications dated January 2020 through March 2025, complemented by manual proceedings screening (SIGGRAPH, CHI, IMX, VRCAI) and backward/forward citation tracking. All databases were last searched in March 2025. Two independent researchers screened all records and extracted data; disagreements were resolved through structured discussion. Bias toward positive novelty outcomes was mitigated by including conference proceedings alongside journal articles to broaden the evidence base. A qualitative thematic synthesis was employed, as methodological heterogeneity across studies precluded statistical meta-analysis. Findings were organized inductively into four thematic domains through iterative coding and inter-author consensus. From an initial corpus of 359 records, 287 unique records were retained after deduplication; following title/abstract screening and full-text eligibility assessment, 64 studies were included in the final synthesis. The majority (60/64) were published between 2021 and 2025, with study sample sizes ranging from small expert cohorts (n ≈ 6) to large public deployments (n > 125). The thematic analysis across technology, interaction design, user experience, and evaluation reveals trends toward participatory, multiuser, and multimodal XR designs, reporting benefits including immersion, engagement, learning, and accessibility, alongside recurring challenges such as cost, usability, cybersickness, content authenticity, and lack of longitudinal evaluation. Beyond thematic description, using a cross-domain analytical synthesis, we identify the Design Coherence Framework for XR Heritage (DCF-XR); this is a four-dimensional interpretive model spanning technology, interaction design, user experience, and evaluation, which provides an original diagnostic lens for understanding the conditions under which XR effectively serves cultural heritage goals. A typology of four recurring design failure modes, derived inductively from the corpus, demonstrates that the most persistent shortcomings in the field arise not from the weakness of individual dimensions but from their misalignment with one another. Evidence is limited by the predominance of small convenience samples, single-session laboratory evaluations, and the absence of domain-specific standardized assessment instruments for XR in CH, which constrains the generalizability of reported outcomes. Targeted recommendations for rigorous, ethical, and inclusive XR practice in CH are presented, highlighting the need for longitudinal studies, open datasets, and standardized evaluation frameworks. This review received no external funding. This review was not pre-registered in a prospective register. Full article

While spatially transmissive or reflective metasurfaces have achieved unprecedented wavefront control in free space, the paradigm shift toward on-chip waveguide-integrated architectures presents novel challenges for constructing compact and scalable photonic systems. Existing on-chip holographic schemes are typically constrained by the complexity of meta-atom structures, limited multiplexing capacity, and strict dependence on specific polarization states. This report comprehensively elucidates a novel on-chip metasurface architecture that relies exclusively on a unified detour phase modulation mechanism to achieve high-capacity, multi-channel holographic multiplexing. By deeply integrating a phase-displacement-joint displacement algorithmic framework with the simulated annealing global optimization algorithm, this design highly circumvents the necessity for complex anisotropic meta-atom geometries and the physical superposition of multiple phase mechanisms. Within an ultra-compact physical footprint of 55.55 × 55.55 μm², the architecture successfully achieves customized holographic reconstruction at specific far-field planes. When discrete TE modes in the visible spectrum are injected from orthogonal lateral directions, distinctly different target holograms are reconstructed in the far field without crosstalk. This mechanism establishes a robust four-wavelength, four-channel independent coding framework. The findings not only elucidate a simplified and highly scalable methodology for ultra-high-density on-chip displays but also provide profound theoretical guidance and technical support for cutting-edge applications such as augmented reality, secure optical communications, and high-density optical data storage. Full article

In recent years, augmented reality has become a popular method of spatial data visualization, both via the most popular and basic plane-based method and more advanced automatic positioning of visualizations based on predefined real-world locations. The aim of this study is to provide new insights into geolocated 3D visualizations in AR using a visual positioning system (VPS). VPS technology enables the creation of visualizations that can be displayed with high accuracy directly on a specific area of interest. This approach is especially well-suited to cultural heritage preservation, as it can be used to visualize destroyed buildings or archaeological sites. The result of the study is a mobile application created using the Unity game engine, which allows users to access AR visualizations as well as additional context in the form of pop-up texts or photographs. Thanks to the display of AR visualization directly at the chosen location, the user can better understand the context of the whole scene. This is because it is a more immersive experience than simply viewing a 3D model on a computer or mobile phone screen. Full article

Projection-based spatial augmented reality (P-SAR) supports appearance-oriented design evaluation by projecting digital materials onto physical mock-ups, but the projected result may deviate from the intended screen-rendered appearance in both colour distribution and normal-induced shading. This paper proposes a screen-referenced, measurement-driven appearance optimisation framework in which a calibrated monitor serves as the visual reference and the projected mock-up as the optimisation target. The workflow combines controlled Unity rendering, colourimetric measurement, D65 CIE Lab analysis, and MATLAB (R2024b)-based iterative update, and separates the problem into albedo appearance optimisation and shading appearance optimisation. The albedo branch uses dominant-colour grouping and CIEDE2000-guided group-wise correction, while the shading branch uses a lightness-contrast descriptor derived from flat and normal-modulated renderings to update the normal-map-driven shading response. Experiments on ten material textures showed that all 23 identified colour groups converged below the adopted $\Delta E_{00}$ threshold of 2.3; the mean texture-level colour difference decreased from 6.24 to 1.36, corresponding to an average reduction of 77.43%. Comparative evaluation showed that the proposed group-wise optimisation outperformed global neutral-grey and global Lab-offset correction baselines. For shading, the mean residual $r_D=|D_{\mathrm{proj}}-D_{\mathrm{ref}}|$ decreased from 1.164 to 0.264 $L^{\ast }$ units, and all ten normal-map cases satisfied the 1.0 $L^{\ast }$ tolerance. A comparison with a global luminance-contrast baseline further supported the benefit of material-level normal-map update over image-domain contrast adjustment. Additional analyses examined the sensitivity to the number of dominant colour groups and clarified the rationale and scope of the adopted thresholds. Integrated photographic examples provided qualitative illustrations of the overall appearance tendency after the complete workflow, while the quantitative assessment was based on colourimetric and lightness-domain measurements. The full workflow required approximately 26 min per material case, indicating practical feasibility for controlled or semi-controlled P-SAR material appearance preparation and design-review scenarios. Full article

Traditional linear models in urban informatics struggle to capture the complex, non-linear spatial non-stationarity inherent in metropolitan housing markets. To overcome these constraints, this study introduces a data-driven computational framework integrating a Spatio-Temporal Graph Convolutional Network (STGCN) with gradient-based Explainable Artificial Intelligence (XAI) and Geographically Weighted Regression (GWR). This framework is empirically tested using 217,598 apartment transactions in Busan, the Republic of Korea, augmented with high-resolution micro-demographic grids and Digital Elevation Model (DEM) topographical data. Utilizing unsupervised K-Means clustering, the region is spatially stratified into a dense Urban Core and a dispersed Suburban Periphery. The STGCN demonstrates overwhelming predictive superiority ($R^2=0.802$) over the traditional Spatial Error Model ($R^2=0.437$). Crucially, gradient-based XAI and localized GWR coefficients successfully unspool the deep learning “black box,” visualizing hyper-localized economic realities that global linear models obscure. The analysis expose stark regional market segmentation driven by environmental topography, mathematically quantifying non-linear dynamics such as coastal high-floor premiums, severe mountainous altitude penalties, and latent urban reconstruction premiums. Ultimately, this research bridges the gap between predictive computational power and spatial economic interpretability, offering a robust informatics framework for equitable urban planning. Full article