Extended Reality: Models and Applications

Topic Information

Dear Colleagues,

Virtual Reality and Augmented Reality have enjoyed exponential growth during the last decade and with similar forecasts. VR is already a consumer technology with hundreds of millions of users and AR is closely following.

Under the umbrella term Extended Reality (including Mixed Reality as well), they are poised to become an essential part of computing and human society, encompassing all types of human–computer interactions and human–human computer-mediated interactions.

While over half a century old, until recently, they were restricted (due to costs and performance) only to a handful of application types. Their recent explosion in diversity and number of applications, similar only to that of generic software from the 1960s or the internet boom, surfaced a clearly insufficient lack of understanding—for both their fundamental inner concepts and workings (such as immersion, presence, perception, interaction modalities, etc.) and development as complex software systems.

Thus, this Topic aims to advance the state of the art in XR, through a focus on sound, well-designed models, experiments, and evaluation methods, covering a wide range of aspects: from core technologies and concepts, to design, user experience, application development, and interdisciplinary aspects.

We welcome submissions presenting original research concepts, experiments, and results, as well as high-quality, rigorous, and useful reviews, on a wide variety of topics. The audience for this Topic includes VR, AR, and MR researchers, developers, industry experts, and end users. Considering the recent growth of XR, we expect a widely multidisciplinary audience—many readers might be unfamiliar with the domain; thus, all papers are expected to be highly accessible in terms of structure, terminology, and gradual introduction of their advanced elements.

Prof. Dr. Moldoveanu Alin
Dr. Anca Morar
Dr. Robert Gabriel Lupu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Electronics electronics	2.6	7.0	2012	16.4 Days	CHF 2400	Submit
Information information	2.9	8.2	2010	20.9 Days	CHF 1800	Submit
Mathematics mathematics	2.2	5.4	2013	17.3 Days	CHF 2600	Submit
Sensors sensors	3.5	9.4	2001	17.8 Days	CHF 2600	Submit

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Published Papers (7 papers)

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All Electronics Information Mathematics Sensors

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20 pages, 1686 KB  

Open AccessReview

Immersive and Multimodal Interfaces for Radar and Spatial Data Visualization in Critical Operational Environments: A Scoping Review

by Jesús Alejandro Isais-Torres, Francisco J. Martínez-Ruiz, Pilar C. Godina González, Juan Lamberto Herrera Martínez, José Ricardo Gómez-Rodríguez and Cristian Eduardo Boyain y Goytia Luna

Information 2026, 17(6), 547; https://doi.org/10.3390/info17060547 - 2 Jun 2026

Viewed by 217

Abstract

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, [...] Read more.

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

(This article belongs to the Topic Extended Reality: Models and Applications)

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14 pages, 1680 KB  

Open AccessArticle

Perceptual Haptic Spectrum Modeling for Fine Texture Rendering on Virtual Object Surfaces in Virtual Reality

by Jinpeng Xu and Bohan Cui

Electronics 2026, 15(10), 2153; https://doi.org/10.3390/electronics15102153 - 17 May 2026

Viewed by 258

Abstract

To enhance immersion in virtual reality (VR) environments and improve the fidelity of virtual tactile interaction, this study proposes a perceptually grounded haptic-rendering framework for fine surface-texture simulation. The framework is centred on a Perceptual Haptic Spectrum Model (PHSM), which maps virtual surface [...] Read more.

To enhance immersion in virtual reality (VR) environments and improve the fidelity of virtual tactile interaction, this study proposes a perceptually grounded haptic-rendering framework for fine surface-texture simulation. The framework is centred on a Perceptual Haptic Spectrum Model (PHSM), which maps virtual surface attributes, including hardness, elasticity, roughness, friction, and microtexture periodicity, to multi-band tactile targets in perceptual frequency space. A Just Noticeable Difference (JND)-inspired parameterisation strategy is used as a design guideline to avoid imperceptible or redundant actuation, while region-specific response functions adapt the output to the fingertip centre, finger pad, and lateral edge. To improve reproducibility, the revised manuscript now specifies the flexible thin-film force/strain-sensor cell, array quantity, 320 Hz per-cell acquisition setting, signal-conditioning pipeline, contact-state classification rules, delay budget, and dual-actuation scheduling logic. The sensing design is based on a commercial flexible piezoresistive force-sensor cell with microsecond-level response time and a 12-bit ADC acquisition chain that provides a sufficient aggregate sampling margin for a 7–21 cell array. Manufacturer-supported sensor performance and prototype-level acceptance criteria are reported for response time, linearity, repeatability, hysteresis, drift, SNR, contact-state detection, latency, and durability. The system remains a proof-of-concept platform rather than a completed large-scale psychophysical validation. Within these boundaries, the results show coherent integration of perceptual modelling, multi-rate sensing, state monitoring, predictive feedforward control, and coordinated haptic actuation for fine VR texture rendering. Full article

(This article belongs to the Topic Extended Reality: Models and Applications)

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20 pages, 10130 KB  

Open AccessArticle

Impact of Audio Feedback on User Experience in Haptic-Visual Mixed Reality Pulse Palpation Training Environments

by Nikitha Donekal Chandrashekar, Shawn D. Safford and Denis Gračanin

Information 2026, 17(5), 399; https://doi.org/10.3390/info17050399 - 22 Apr 2026

Viewed by 373

Abstract

Background: Mixed Reality (MR) environments rely on multimodal feedback to enrich sensory integration and realism, which enhances User Experience (UX). Prior studies have shown the benefits of haptic feedback in audio–visual MR medical training environments, but researchers have not fully examined how [...] Read more.

Background: Mixed Reality (MR) environments rely on multimodal feedback to enrich sensory integration and realism, which enhances User Experience (UX). Prior studies have shown the benefits of haptic feedback in audio–visual MR medical training environments, but researchers have not fully examined how audio cues influence Haptic–Visual (HV) training environments. Methods: We built a high-fidelity MR medical training environment that synchronized visual, haptic, and audio of the human pulse. We conducted a between-subjects study with thirty novice participants who performed pulse palpation tasks in HV and Haptic–Audio–Visual (HAV) modalities. We employ a multidimensional UX evaluation by measuring task performance, presence, usability, and task workload to assess the impact of adding audio feedback in MR pulse palpation training environments. Results: Participants in the HAV modality performed tasks more accurately and reported stronger presence and higher usability. They did not report any significant increase in workload compared to the HV modality. Conclusions: Audio feedback improved perceptual coherence and enhanced UX in pulse palpation tasks. Our findings highlight the training value of integrating multimodal feedback in MR pulse palpation training systems and provide practical guidelines for designing more immersive and effective MR environments. Full article

(This article belongs to the Topic Extended Reality: Models and Applications)

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33 pages, 66840 KB  

Open AccessArticle

VR Human-Centric Winter Lane Detection: Performance and Driving Experience Evaluation

by Tatiana Ortegon-Sarmiento, Patricia Paderewski, Sousso Kelouwani, Francisco Gutierrez-Vela and Alvaro Uribe-Quevedo

Sensors 2025, 25(20), 6312; https://doi.org/10.3390/s25206312 - 12 Oct 2025

Viewed by 1492

Abstract

Driving in snowy conditions challenges both human drivers and autonomous systems. Snowfall and ice accumulation impair vehicle control and affect driver perception and performance. Road markings are often obscured, forcing drivers to rely on intuition and memory to stay in their lane, which [...] Read more.

Driving in snowy conditions challenges both human drivers and autonomous systems. Snowfall and ice accumulation impair vehicle control and affect driver perception and performance. Road markings are often obscured, forcing drivers to rely on intuition and memory to stay in their lane, which can lead to encroachment into adjacent lanes or sidewalks. Current lane detectors assist in lane keeping, but their performance is compromised by visual disturbances such as ice reflection, snowflake movement, fog, and snow cover. Furthermore, testing these systems with users on actual snowy roads involves risks to driver safety, equipment integrity, and ethical compliance. This study presents a low-cost virtual reality simulation for evaluating winter lane detection in controlled and safe conditions from a human-in-the-loop perspective. Participants drove in a simulated snowy scenario with and without the detector while quantitative and qualitative variables were monitored. Results showed a 49.9% reduction in unintentional lane departures with the detector and significantly improved user experience, as measured by the UEQ-S (p = 0.023, Cohen’s d = 0.72). Participants also reported higher perceived safety, situational awareness, and confidence. These findings highlight the potential of vision-based lane detection systems adapted to winter environments and demonstrate the value of immersive simulations for user-centered testing of ADASs. Full article

(This article belongs to the Topic Extended Reality: Models and Applications)

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25 pages, 66105 KB  

Open AccessFeature PaperArticle

Toward Real-Time Scalable Rigid-Body Simulation Using GPU-Optimized Collision Detection and Response

by Nak-Jun Sung and Min Hong

Mathematics 2025, 13(19), 3230; https://doi.org/10.3390/math13193230 - 9 Oct 2025

Viewed by 3541

Abstract

We propose a GPU-parallelized collision-detection and response framework for rigid-body dynamics, designed to efficiently handle densely populated 3D simulations in real time. The method combines explicit Euler time integration with a hierarchical Octree–AABB collision-detection scheme, enabling early pruning and localized refinement of contact [...] Read more.

We propose a GPU-parallelized collision-detection and response framework for rigid-body dynamics, designed to efficiently handle densely populated 3D simulations in real time. The method combines explicit Euler time integration with a hierarchical Octree–AABB collision-detection scheme, enabling early pruning and localized refinement of contact checks. To resolve collisions, we employ a two-step response algorithm that integrates non-penetration correction and impulse-based velocity updates, stabilized through smoothing, clamping, and bias mechanisms. The framework is fully implemented within Unity3D using compute shaders and optimized GPU kernels. Experiments across multiple mesh models and increasing object counts demonstrate that the proposed hierarchical configuration significantly improves scalability and frame stability compared to conventional flat AABB methods. In particular, a two-level hierarchy achieves the best trade-off between spatial resolution and computational cost, maintaining interactive frame rates (≥30 fps) under high-density scenarios. These results suggest the practical applicability of our method to real-time simulation systems involving complex collision dynamics. Full article

(This article belongs to the Topic Extended Reality: Models and Applications)

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34 pages, 1781 KB  

Open AccessSystematic Review

Assessing Human Factors in Virtual Reality Environments for Industry 5.0: A Comprehensive Review of Factors, Metrics, Techniques, and Future Opportunities

by Oscar Escallada, Ganix Lasa, Maitane Mazmela, Ainhoa Apraiz, Nagore Osa and Hien Nguyen Ngoc

Information 2025, 16(1), 35; https://doi.org/10.3390/info16010035 - 8 Jan 2025

Cited by 8 | Viewed by 5136

Abstract

Industry 5.0, the latest evolution in industrial processes, builds upon the principles of Industry 4.0 by emphasizing human-centric approaches and the integration of virtual reality technologies. This paradigm shift underscores the importance of collaboration between humans and advanced technologies with a focus on [...] Read more.

Industry 5.0, the latest evolution in industrial processes, builds upon the principles of Industry 4.0 by emphasizing human-centric approaches and the integration of virtual reality technologies. This paradigm shift underscores the importance of collaboration between humans and advanced technologies with a focus on optimizing efficiency, safety, and worker skill development. Based on the PRISMA 2020 guidelines, this study conducts a systematic literature review, identifying 328 papers from databases. After applying inclusion and exclusion criteria, 24 papers were selected for detailed analysis. The review provides valuable insights into the diverse evaluation methods employed in the literature, and a detailed classification of 29 human factors with their associated metrics. Despite the absence of a standardized method for assessing human factors in VR experiences, this comprehensive analysis of 240 different ways of measuring factors highlights the current state of evaluating human-centered VR experiences in Industry 5.0. While the review reveals some limitations such as potential bias in study selection and heterogeneity of methods, it also identifies significant research gaps and proposes future directions. This study contributes to the establishment of a coherent structure for future research and development in human-centered design within the rapidly evolving landscape of Industry 5.0, paving the way for more effective and standardized approaches in the future. Full article

(This article belongs to the Topic Extended Reality: Models and Applications)

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27 pages, 28326 KB  

Open AccessArticle

Full-Body Pose Estimation of Humanoid Robots Using Head-Worn Cameras for Digital Human-Augmented Robotic Telepresence

by Youngdae Cho, Wooram Son, Jaewan Bak, Yisoo Lee, Hwasup Lim and Youngwoon Cha

Mathematics 2024, 12(19), 3039; https://doi.org/10.3390/math12193039 - 28 Sep 2024

Cited by 3 | Viewed by 4791

Abstract

We envision a telepresence system that enhances remote work by facilitating both physical and immersive visual interactions between individuals. However, during robot teleoperation, communication often lacks realism, as users see the robot’s body rather than the remote individual. To address this, we propose [...] Read more.

We envision a telepresence system that enhances remote work by facilitating both physical and immersive visual interactions between individuals. However, during robot teleoperation, communication often lacks realism, as users see the robot’s body rather than the remote individual. To address this, we propose a method for overlaying a digital human model onto a humanoid robot using XR visualization, enabling an immersive 3D telepresence experience. Our approach employs a learning-based method to estimate the 2D poses of the humanoid robot from head-worn stereo views, leveraging a newly collected dataset of full-body poses for humanoid robots. The stereo 2D poses and sparse inertial measurements from the remote operator are optimized to compute 3D poses over time. The digital human is localized from the perspective of a continuously moving observer, utilizing the estimated 3D pose of the humanoid robot. Our moving camera-based pose estimation method does not rely on any markers or external knowledge of the robot’s status, effectively overcoming challenges such as marker occlusion, calibration issues, and dependencies on headset tracking errors. We demonstrate the system in a remote physical training scenario, achieving real-time performance at 40 fps, which enables simultaneous immersive and physical interactions. Experimental results show that our learning-based 3D pose estimation method, which operates without prior knowledge of the robot, significantly outperforms alternative approaches requiring the robot’s global pose, particularly during rapid headset movements, achieving markerless digital human augmentation from head-worn views. Full article

(This article belongs to the Topic Extended Reality: Models and Applications)

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