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Human-Computer Interaction and Artificial Intelligence in VR/AR/MR Application

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (20 February 2025) | Viewed by 11481

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Special Issue Editors

Prof. Dr. Kibum Kim

E-Mail Website
Guest Editor

Department of Human-Computer Interaction, Hanyang University, Ansan 15588, Republic of Korea
Interests: human–computer interaction; virtual reality; augmented reality, mixed reality; assistive technology; serious game; multimodal user interfaces
Special Issues, Collections and Topics in MDPI journals

Dr. Huawei Tu

E-Mail Website
Guest Editor

Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC 3086, Australia
Interests: human-computer interaction; virtual interaction

Special Issue Information

Dear Colleagues,

AI (Artificial Intelligence) and XR (eXtended Reality) technologies are in the spotlight as key technologies in the fourth industrial revolution, and the field of HCI (Human–Computer Interaction) is also emerging as a major trend in research. AI and XR technologies are widely utilized in all areas in which data are produced and in all areas that require the Metaverse, respectively. AI algorithms and modeling technologies are rapidly developing, with the industry releasing lightweight and high-performance XR devices based on the computing power of advanced computer hardware. When people apply these technologies, the HCI field offers various methodologies to help users utilize them properly and safely. This Special Issue focuses on state-of-the-art research in various new computer-science-based technologies, including AI, machine learning, deep learning, computer vision, virtual reality, augmented reality, extended reality, and HCI-based convergence and application technologies. This Special Issue aims to inspire and motivate many researchers in the field of computer science by illuminating the development of various technologies, and discovering and sharing fields that can be applied to them.

Prof. Dr. Kibum Kim
Dr. Huawei Tu
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

VR/AR/MR applications: manufacturing, healthcare, virtual travel, e-sports, games, cultural heritage, military, e-commerce, military, psychology, medicine, emergency response, entertainment, engineering, advertising, etc.
VR/AR/MR collaboration
context awareness for VR/AR
education with VR/AR/MR
display technologies for VR/AR/MR
human–computer interactions in VR/AR/MR
human factors in VR/AR/MR
perception/presence in VR/AR/MR
physiological sensing for VR/AR/MR
user experience/usability in VR/AR/MR
interfaces for VR/AR
virtual humans/avatars in VR/AR/MR
wellbeing with VR/AR/MR
human behavior sensing
gesture interface
interactive simulation
new interaction design for VR/AR/MR
AR/VR devices and technologies integrated
issues on real world and virtual world integration
social aspects in VR/AR/MR interaction
autonomic computing and communication
multi-agent systems
agile software systems
engineered self-organization and self-organizing computing systems
swarms and swarm intelligence
pervasive and mobile computing
sensor networks
P2P and cloud computing
web and participatory systems
self-properties and adaptive algorithms
operating systems and middleware for autonomous and adaptive systems
social sensing and social analysis

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Published Papers (6 papers)

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Research

29 pages, 4981 KiB

Open AccessArticle

SRD Method: Integrating Autostereoscopy and Gesture Interaction for Immersive Serious Game-Based Behavioral Skills Training

by Linkai Lyu, Tianrui Hu, Hongrun Wang and Wenjun Hou

Electronics 2025, 14(7), 1337; https://doi.org/10.3390/electronics14071337 - 27 Mar 2025

Viewed by 235

Abstract

This study focuses on the innovative application of HCI and XR technologies in behavioral skills training (BST) in the digital age, exploring their potential in education, especially experimental training. Despite the opportunities these technologies offer for immersive BST, traditional methods remain mainstream, with XR devices like HMDs causing user discomfort and current research lacking in evaluating user experience. To address these issues, we propose the spatial reality display (SRD) method, a new BST approach based on spatial reality display. This method uses autostereoscopic technology to avoid HMD discomfort, employs intuitive gesture interactions to reduce learning costs, and integrates BST content into serious games (SGs) to enhance user acceptance. Using the aluminothermic reaction in chemistry experiments as an example, we developed a Unity3D-based XR application allowing users to conduct experiments in a 3D virtual environment. Our study compared the SRD method with traditional BST through simulation, questionnaires, and interviews, revealing significant advantages of SRD in enhancing user skills and intrinsic motivation. Full article

(This article belongs to the Special Issue Human-Computer Interaction and Artificial Intelligence in VR/AR/MR Application)

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27 pages, 9531 KiB

Open AccessArticle

The Creation of Virtual Stands in the Metaverse: Applications for the Textile Sector

by Vicente Jover, Silvia Sempere and Santiago Ferrándiz

Electronics 2025, 14(2), 359; https://doi.org/10.3390/electronics14020359 - 17 Jan 2025

Viewed by 1201

Abstract

This study explores the potential of the Metaverse in designing trade-fair stands for the exhibition of products and services related to the textile sector, with a particular focus on upholstery fabrics. The primary aim is to enhance relations between manufacturers and customers by leveraging virtual environments. This research is part of a broader investigation into the applications of the Metaverse within the framework of Industry 5.0, emphasizing design aspects and user engagement. The applied methodology seeks to extend the functionalities of traditional physical stands while offering a viable alternative for the promotion and sale of textile products. To create the digital environment, the Metaverse platform, Spatial.io, and its Spatial Toolkit—featuring a Unity-based template which allows for the more efficient editing of scenarios in their three-dimensional aspects—were employed. Although the template is subject to certain limitations imposed by Spatial, such as restricted access to Unity’s full capabilities, these constraints were mitigated through effective programming. This enabled the incorporation of a texture selector, allowing users to combine and visualize fabric options dynamically. The authors of this article have designed a virtual stand in alignment with client specifications by applying compositional resources grounded in basic design principles of modernity and simple structural systems. This virtual stand was tested at the Habitat Fair in Valencia 2024. The results demonstrate the promising potential of these emerging technologies in advancing the capabilities of the textile sector. Full article

(This article belongs to the Special Issue Human-Computer Interaction and Artificial Intelligence in VR/AR/MR Application)

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14 pages, 4843 KiB

Open AccessArticle

Enhanced Multi-Scale Attention-Driven 3D Human Reconstruction from Single Image

by Yong Ren, Mingquan Zhou, Pengbo Zhou, Shibo Wang, Yangyang Liu, Guohua Geng, Kang Li and Xin Cao

Electronics 2024, 13(21), 4264; https://doi.org/10.3390/electronics13214264 - 30 Oct 2024

Viewed by 1392

Abstract

Due to the inherent limitations of a single viewpoint, reconstructing 3D human meshes from a single image has long been a challenging task. While deep learning networks enable us to approximate the shape of unseen sides, capturing the texture details of the non-visible side remains difficult with just one image. Traditional methods utilize Generative Adversarial Networks (GANs) to predict the normal maps of the non-visible side, thereby inferring detailed textures and wrinkles on the model’s surface. However, we have identified challenges with existing normal prediction networks when dealing with complex scenes, such as a lack of focus on local features and insufficient modeling of spatial relationships.To address these challenges, we introduce EMAR—Enhanced Multi-scale Attention-Driven Single-Image 3D Human Reconstruction. This approach incorporates a novel Enhanced Multi-Scale Attention (EMSA) mechanism, which excels at capturing intricate features and global relationships in complex scenes. EMSA surpasses traditional single-scale attention mechanisms by adaptively adjusting the weights between features, enabling the network to more effectively leverage information across various scales. Furthermore, we have improved the feature fusion method to better integrate representations from different scales. This enhanced feature fusion allows the network to more comprehensively understand both fine details and global structures within the image. Finally, we have designed a hybrid loss function tailored to the introduced attention mechanism and feature fusion method, optimizing the network’s training process and enhancing the quality of reconstruction results. Our network demonstrates significant improvements in performance for 3D human model reconstruction. Experimental results show that our method exhibits greater robustness to challenging poses compared to traditional single-scale approaches. Full article

(This article belongs to the Special Issue Human-Computer Interaction and Artificial Intelligence in VR/AR/MR Application)

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14 pages, 1632 KiB

Open AccessArticle

The Level of Physical Activity, E-Game-Specific Reaction Time, and Self-Evaluated Health and Injuries’ Occurrence in Non-Professional Esports Players

by Magdalena Cyma-Wejchenig, Janusz Maciaszek, Julia Ciążyńska and Rafał Stemplewski

Electronics 2024, 13(12), 2328; https://doi.org/10.3390/electronics13122328 - 14 Jun 2024

Cited by 2 | Viewed by 3059

Abstract

This study aims to compare physical activity levels, esports-specific reaction times, self-evaluated health, and injuries between non-professional esports players (EPs) and non-players (NPs). Participants were healthy, with an average age of 22.7 ± 2.49 years and BMI of 25.5 ± 4.95 kg/m². Physical activity was quantified using the Baecke Questionnaire, while reaction times were measured with computer games. The analysis reveals that EPs exhibit significantly lower levels of physical activity compared to NPs (p < 0.05), underscoring the sedentary nature of esports. Despite this, EPs demonstrate superior reaction times (p < 0.001), suggesting cognitive enhancements associated with esports participation. EPs report increased incidences of gameplay-related discomfort (p = 0.025), highlighting health drawbacks of prolonged gaming. However, no significant differences were observed in overall self-evaluated health statuses and specific pain complaints between the groups, indicating a complex relationship between esports involvement and perceived health outcomes. These findings suggest that esports can offer cognitive benefits through improved reaction times but are also associated with reduced physical activity and increased reporting of discomfort. This dichotomy underscores the need for strategies that capitalize on the cognitive advantages of esports while mitigating its physical health risks, encouraging a more balanced engagement with the activity. Full article

(This article belongs to the Special Issue Human-Computer Interaction and Artificial Intelligence in VR/AR/MR Application)

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17 pages, 3222 KiB

Open AccessEditor’s ChoiceArticle

Dynamic Difficulty Adaptation Based on Stress Detection for a Virtual Reality Video Game: A Pilot Study

by Carmen Elisa Orozco-Mora, Rita Q. Fuentes-Aguilar and Gustavo Hernández-Melgarejo

Electronics 2024, 13(12), 2324; https://doi.org/10.3390/electronics13122324 - 14 Jun 2024

Cited by 1 | Viewed by 2099

Abstract

Virtual reality (VR) is continuing to grow as more affordable technological devices become available. Video games are one of the most profitable applications, while rehabilitation has the most significant social impact. Both applications require a proper user evaluation to provide personalized experiences that avoid boring or stressful situations. Despite the successful applications, there are several opportunities to improve the field of human–machine interactions, one of the most popular ones being the use of affect detection to create personalized experiences. In that sense, this study presents the implementation of two dynamic difficulty adaptation strategies. The person’s affective state is estimated through a machine learning classification model, which later serves to adapt the difficulty of the video game online. The results show that it is possible to maintain the user at a given difficulty level, which is analogous to achieving the well-known flow state. Among the two implemented strategies, no statistical differences were found in the workload induced by the users. However, more physical demands and a higher frustration were induced by one of the strategies, validated with the recorded muscular activity. The results obtained contribute to the state of the art of DDA strategies in virtual reality driven by affective data. Full article

(This article belongs to the Special Issue Human-Computer Interaction and Artificial Intelligence in VR/AR/MR Application)

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20 pages, 17657 KiB

Open AccessArticle

DiT-Gesture: A Speech-Only Approach to Stylized Gesture Generation

by Fan Zhang, Zhaohan Wang, Xin Lyu, Naye Ji, Siyuan Zhao and Fuxing Gao

Electronics 2024, 13(9), 1702; https://doi.org/10.3390/electronics13091702 - 27 Apr 2024

Viewed by 1957

Abstract

The generation of co-speech gestures for digital humans is an emerging area in the field of virtual human creation. Prior research has progressed by using acoustic and semantic information as input and adopting a classification method to identify the person’s ID and emotion for driving co-speech gesture generation. However, this endeavor still faces significant challenges. These challenges go beyond the intricate interplay among co-speech gestures, speech acoustic, and semantics; they also encompass the complexities associated with personality, emotion, and other obscure but important factors. This paper introduces “DiT-Gestures”, a speech-conditional diffusion-based and non-autoregressive transformer-based generative model with the WavLM pre-trained model and a dynamic mask attention network (DMAN). It can produce individual and stylized full-body co-speech gestures by only using raw speech audio, eliminating the need for complex multimodal processing and manual annotation. Firstly, considering that speech audio contains acoustic and semantic features and conveys personality traits, emotions, and more subtle information related to accompanying gestures, we pioneer the adaptation of WavLM, a large-scale pre-trained model, to extract the style from raw audio information. Secondly, we replace the causal mask by introducing a learnable dynamic mask for better local modeling in the neighborhood of the target frames. Extensive subjective evaluation experiments are conducted on the Trinity, ZEGGS, and BEAT datasets to confirm WavLM’s and the model’s ability to synthesize natural co-speech gestures with various styles. Full article

(This article belongs to the Special Issue Human-Computer Interaction and Artificial Intelligence in VR/AR/MR Application)

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