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Emerging Technologies of Human-Computer Interaction
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Emerging Technologies of Human-Computer Interaction

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 July 2025 | Viewed by 4327

Special Issue Editor

Dr. Tibor Guzsvinecz

E-Mail Website
Guest Editor
Department of Information Technology and its Applications, University of Pannonia, 8200 Veszprém, Hungary
Interests: data science; human–computer interaction; spatial ability; video games; virtual reality
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of technology has fundamentally transformed the way humans interact with computers. This Special Issue on emerging technologies of human–computer interaction (HCI) seeks to explore the cutting-edge innovations that are shaping the future of HCI. The aim of this Special Issue is to provide a comprehensive overview of the latest research, development, and applications in this dynamic field. 

Topics of interest include, but are not limited to, the following:

  • Novel interaction techniques;
  • Immersive environments and smart environments;
  • Adaptive interfaces;
  • Wearable technologies;
  • Gesture recognition;
  • User experience design;
  • User-centered design;
  • Video games;
  • Assistive technologies;
  • AI-driven interaction models.

Researchers, practitioners, and industry experts are invited to submit their original research, case studies, and review articles that contribute to the understanding and advancement of HCI technologies. This Special Issue will serve as a valuable resource for those looking to stay ahead in the rapidly evolving landscape of human–computer interaction.

Dr. Tibor Guzsvinecz
Guest Editor

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. Applied Sciences 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

  • human–computer interaction
  • immersive environments
  • interaction models
  • smart environments
  • user experience

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

Jump to: Review

17 pages, 6197 KiB  
Article
Wormhole Whispers: Reflecting User Privacy Data Boundaries Through Algorithm Visualization
by Xiaoxiao Wang, Jingjing Zhang, Huize Wan and Yuan Yao
Appl. Sci. 2025, 15(4), 2034; https://doi.org/10.3390/app15042034 - 15 Feb 2025
Viewed by 398
Abstract
As user interactions on online social platforms increase, so does the public’s concern over the exposure of user privacy data. However, ordinary users often lack a clear and intuitive understanding of how their personal online information flows and how algorithms are involved in [...] Read more.
As user interactions on online social platforms increase, so does the public’s concern over the exposure of user privacy data. However, ordinary users often lack a clear and intuitive understanding of how their personal online information flows and how algorithms are involved in this process. We have developed a design to address this issue that visualizes data algorithms and transmission based on data collection and analysis. The primary contribution of this work is an innovative approach that uses metaphorical visual language to represent the flow of user data, algorithms, and information. It also aims to present these concepts interactively, allowing users to gain a more vivid understanding of the dynamic changes in their personal data. This participatory and interactive design seeks to provide users, as well as various stakeholders, an opportunity to reflect on the relationship between online society, privacy data boundaries, and users’ rights to be informed. Full article
(This article belongs to the Special Issue Emerging Technologies of Human-Computer Interaction)
►▼ Show Figures

Figure 1

Figure 1
<p>Privacy policy and SDK data collection analysis and visualization flowchart. (<b>a</b>) Collecting and organizing user privacy policies of the five most popular social media platforms. (<b>b</b>) Collecting and organizing a third-party SDK data-sharing checklist.</p> Full article ">Figure 2
<p>Organization of user privacy policies from the five most popular social media platforms.</p> Full article ">Figure 3
<p>The basic process of design methodology, when combined with the wormhole metaphor, establishes the foundation for this visualization’s metaphorical logic.</p> Full article ">Figure 4
<p>Visualization diagram of clustering algorithm: dividing planets into different galaxies based on their characteristics.</p> Full article ">Figure 5
<p>Visual diagram of collaborative filtering algorithm: user-centered filtering of content.</p> Full article ">Figure 6
<p>Visual diagram of association rule learning algorithm: discovering patterns in a large number of planets (contents).</p> Full article ">Figure 7
<p>The four components of interaction design transformation.</p> Full article ">
14 pages, 3217 KiB  
Article
CMDAF: Cross-Modality Dual-Attention Fusion Network for Multimodal Sentiment Analysis
by Wang Guo, Kun Su, Bin Jiang, Kang Xie and Jie Liu
Appl. Sci. 2024, 14(24), 12025; https://doi.org/10.3390/app142412025 - 23 Dec 2024
Viewed by 1080
Abstract
Multimodal sentiment analysis (MSA) seeks to predict subjective human sentiments by utilizing information from multiple modalities. It has been applied in diverse scenarios. Recent studies suggest that MSA benefits from integrating diverse modalities, emphasizing the fusion of multimodal information at different levels and [...] Read more.
Multimodal sentiment analysis (MSA) seeks to predict subjective human sentiments by utilizing information from multiple modalities. It has been applied in diverse scenarios. Recent studies suggest that MSA benefits from integrating diverse modalities, emphasizing the fusion of multimodal information at different levels and the joint learning of modality-consistent and modality-inconsistent representations. In this work, we put forward a cross-modality dual-attention fusion network (CMDAF) that incorporates a cross-modality dual-attention fusion module. This module facilitates the integration of representations across modalities, enabling effective fusion from shallow to deep levels. A Transformer-based projection module transforms the fused representations into modality-consistent and modality-inconsistent forms, further enhanced through multi-scale contrast learning. Experiments on three MSA datasets (MOSI, MOSEI, and CH-SIMS) demonstrate the superiority of CMDAF, achieving state-of-the-art performance on most metrics. Full article
(This article belongs to the Special Issue Emerging Technologies of Human-Computer Interaction)
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Figure 1

Figure 1
<p>The overall architecture of the CMDAF. For the raw data, the network obtains unimodal features by pre-training feature extractors. The architecture comprises the following three essential components: the cross-modality dual-attention fusion module, the feature projection module, and the multi-loss learning module.</p> Full article ">Figure 2
<p>The network structure of the cross-modality dual-attention module.</p> Full article ">Figure 3
<p>The network structure of the features projection module.</p> Full article ">Figure 4
<p>The T-SNE visualization compares the feature representations of three modalities between (<b>a</b>) samples extracted using the MOSI method and (<b>b</b>) samples obtained through the pre-trained extractors of our proposed method.</p> Full article ">
24 pages, 6838 KiB  
Article
Affective Stroking: Design Thermal Mid-Air Tactile for Assisting People in Stress Regulation
by Sheng He, Hao Zeng, Mengru Xue, Guanghui Huang, Cheng Yao and Fangtian Ying
Appl. Sci. 2024, 14(20), 9494; https://doi.org/10.3390/app14209494 - 17 Oct 2024
Viewed by 1260
Abstract
Haptics for stress regulation is well developed these years. Using vibrotactile to present biofeedback, guiding breathing or heartbeat regulation is a dominant technical approach. However, designing computer-mediated affective touch for stress regulation is also a promising way and has not been fully explored. [...] Read more.
Haptics for stress regulation is well developed these years. Using vibrotactile to present biofeedback, guiding breathing or heartbeat regulation is a dominant technical approach. However, designing computer-mediated affective touch for stress regulation is also a promising way and has not been fully explored. In this paper, a haptic device was developed to test whether the computer-mediated affective stroking on the forearm could help to assist people in reducing stress. In our method, we used mid-air technology to generate subtle pressure force by blowing air and generating thermal feedback by using Peltier elements simultaneously. Firstly, we found intensity and velocity parameters to present comfort and pleasant stroking sensations. Afterward, an experiment was conducted to find out whether this approach could help people mediate their perceived and physiological stress. A total of 49 participants were randomly assigned to either a Stroking Group (SG) or a Control Group (CG). Results showed that participants from SG felt more relaxed than those from CG. The physiological stress index, RMSSD, increased and LF/HF decreased in SG although these changes were not statistically significant. Our exploration created subtle, non-invasive, noiseless haptic sensations. It could be a promising alternative for assisting people in stress regulation. Design implications and future applicable scenarios were discussed. Full article
(This article belongs to the Special Issue Emerging Technologies of Human-Computer Interaction)
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Figure 1

Figure 1
<p>Hardware design: (<b>a</b>) hardware of the stroking device and (<b>b</b>) framework of the whole system.</p> Full article ">Figure 2
<p>Arrangements of 8 fans and blowing-air pressure on the skin to imitate stroking sensation.</p> Full article ">Figure 3
<p>Fans around the outer and upper sides of the arm, blowing air pressure on the forearm.</p> Full article ">Figure 4
<p>The two-way interaction of Intensity × Duration on four metrics: (<b>a</b>) perceived continuity; (<b>b</b>) perceived authenticity; (<b>c</b>) perceived comfort; and (<b>d</b>) perceived pleasantness.</p> Full article ">Figure 5
<p>Force generated by a single fan blowing once for 3 s (<b>a</b>) and 5 s (<b>b</b>).</p> Full article ">Figure 6
<p>Comparison of pressure parameters caused by air and human touch for fast stroking.</p> Full article ">Figure 7
<p>Comparison of pressure parameters caused by air and human touch for slow stroking.</p> Full article ">Figure 8
<p>Experiment design: (<b>a</b>) experiment setup and (<b>b</b>) experiment environment.</p> Full article ">Figure 9
<p>Experiment Procedure.</p> Full article ">Figure 10
<p>STAI scores: (<b>a</b>) mean scores across three phases and (<b>b</b>) changes during the relaxation phase. (*: <span class="html-italic">p</span> &lt; 0.050).</p> Full article ">Figure 11
<p>RMSSD metrics: (<b>a</b>) mean RMSSD metrics across three phases and (<b>b</b>) changes in RMSSD metrics during the relaxation phase.</p> Full article ">Figure 12
<p>LF/HF metrics: (<b>a</b>) mean LF/HF metrics across three phases and (<b>b</b>) changes in LF/HF metrics during the relaxation phase. (*: abnormal value).</p> Full article ">

Review

Jump to: Research

31 pages, 4490 KiB  
Review
Uncovering Research Trends on Artificial Intelligence Risk Assessment in Businesses: A State-of-the-Art Perspective Using Bibliometric Analysis
by Juan Carlos Muria-Tarazón, Juan Vicente Oltra-Gutiérrez, Raúl Oltra-Badenes and Santiago Escobar-Román
Appl. Sci. 2025, 15(3), 1412; https://doi.org/10.3390/app15031412 - 30 Jan 2025
Viewed by 823
Abstract
This paper presents a quantitative vision of the study of artificial intelligence risk assessment in business based on a bibliometric analysis of the most relevant publications. The main goal is to determine whether the risk assessment of artificial intelligence systems used in businesses [...] Read more.
This paper presents a quantitative vision of the study of artificial intelligence risk assessment in business based on a bibliometric analysis of the most relevant publications. The main goal is to determine whether the risk assessment of artificial intelligence systems used in businesses is really a subject of increasing interest and to identify the most influential and productive sources of scientific research in this area. Data were collected from the Web of Science Core Collection, one of the most complete and prestigious databases. Regarding the temporal evolution of publications and citations this study evidences, this research subject shows rapid growth in the number of publications (at a compound annual rate of 31.20% from 2018 to 2024 inclusive), showing its high attraction for researchers, responding to the need to implement systematic risk assessment processes in the organizations using AI to mitigate potential harms, ensure compliance with regulations, and enhance artificial intelligence systems’ trust and adoption. Especially after the surge of large language models like ChatGPT or Gemini, AI is revolutionizing the dynamics of human–computer interaction using natural language, video, and audio. However, as the scientific community initiates rigorous studies on AI risk assessment within organizational contexts, it is imperative to consider critical issues such as data privacy, ethics, bias, and hallucinations to ensure the successful integration and interaction of AI systems with human operators. Furthermore, this paper constitutes a starting point, including for any researcher who wants to be introduced to this topic, indicating new challenges that should be dealt by researchers interested in AI and hot topics, in addition to the most relevant literature, authors, and journals about this research subject. Full article
(This article belongs to the Special Issue Emerging Technologies of Human-Computer Interaction)
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Figure 1

Figure 1
<p>Flowchart of the bibliometric research strategy.</p> Full article ">Figure 2
<p>Number of annual publications according to WoS Core Collection query on artificial intelligence’s business risk assessment (TPS) vs. annual publications in AI (TPAI).</p> Full article ">Figure 3
<p>Citations between institutions. Overlay visualization via VOSviewer.</p> Full article ">Figure 4
<p>Corresponding author’s countries and diversity of nationalities.</p> Full article ">Figure 5
<p>Collaboration between countries.</p> Full article ">Figure 6
<p>Bibliometrix 25-word treemap (Keywords Plus).</p> Full article ">Figure 7
<p>Bibliometrix 25-word treemap (author’s keywords).</p> Full article ">Figure 8
<p>Trends in the co-occurrence of author’s keywords over time.</p> Full article ">Figure 9
<p>SciMat overlapping map and document count evolution map.</p> Full article ">Figure 10
<p>SciMat document count strategic map.</p> Full article ">Figure 11
<p>SciMat cluster networks.</p> Full article ">
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