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	5.3	2012	16.4 Days	CHF 2400	Submit
Information information	2.4	6.9	2010	16.4 Days	CHF 1600	Submit
Mathematics mathematics	2.3	4.0	2013	18.3 Days	CHF 2600	Submit
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600	Submit

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Published Papers (2 papers)

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All Electronics Information Mathematics Sensors

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34 pages, 1781 KiB  

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

Viewed by 577

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 KiB  

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 1 | Viewed by 1159

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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