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

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

Deadline for manuscript submissions: closed (25 June 2024) | Viewed by 2208

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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

Published Papers (3 papers)

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Research

14 pages, 1632 KiB  
Article
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
Viewed by 372
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 [...] Read more.
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  
Article
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
Viewed by 612
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 [...] Read more.
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  
Article
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 777
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 [...] Read more.
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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