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Intelligent Human-Computer Interaction Systems and Their Evaluation

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 10 November 2024 | Viewed by 2556

Special Issue Editors


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Guest Editor
Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, Slovenia
Interests: computer–human interaction; user experience; IoT; web technology; intelligent user interfaces; accessibility; technologies for touchless HCI
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, Slovenia
Interests: empirical research methods; operations research; behavioral operations research; sensor-based process optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in modern user interfaces (UI) and artificial intelligence (AI) have enabled the development of intelligent user interfaces (IUI) that allow new ways of interacting with devices. Although the idea of introducing intelligence into human–computer interaction (HCI) and UIs has been around for a long time, only recently have we seen the rise of intelligent HCI solutions being developed for various domains. Developments in the Internet of Things (IoT) technologies and new AI methods and algorithms for intelligent HCI certainly contributed to this. Intelligent solutions based on sensor technology, IoT, AI methods and algorithms can leverage various information about users, such as their behaviour, interests, preferences, etc., for enriching devices with IUIs and natural HCI that enable improved productivity, efficiency, effectiveness, and user experience (UX). There is a need for novel AI methods and algorithms which will be able to use data obtained from various sensor technologies for pattern recognition and personalization of UIs and HCI according to the characteristics and capabilities of the user. At the same time, we must not forget about the appropriate models and methods that will enable the proper evaluation of such solutions. Many theories and models exist in the existing literature, but they were validated in the context of solutions that were not based on intelligent HCI. For this reason, it is necessary to develop new models and methods in the field of intelligent HCI and to test them empirically if we want to provide adequate tools for UX evaluation of modern intelligent HCI solutions.

This Special Issue is dedicated to exploring current trends and challenges in research, as well as opportunities related to intelligent HCIs from the following perspectives:

  1. This Special Issue seeks novel AI solutions for innovative HCI solutions that will improve accessibility, trust, UX, and explainability of interactive intelligent systems.
  2. The Special Issue is dedicated to understanding how new advances in intelligent HCI and its applications can be applied in various contexts such as education, healthcare, smart home, tourism, etc. The key aim of this Special Issue is to bring together state-of-the-art research and innovative HCI solutions for IUIs, in addition to discoveries, new ideas, and innovative improvements.
  3. The Special Issue is also dedicated to collecting newly developed and validated theories and models for empirical evaluation of intelligent UX.

Special Issue topics include, but are not limited to:

  • IoT technology integration in intelligent user interfaces (IUIs);
  • Human–computer interaction (HCI) in IUIs;
  • AI solutions for HCI;
  • Novel AI methods and algorithms for HCI;
  • Machine learning for HCI;
  • Personalized predictions for IUIs;
  • Design patterns for IUIs;
  • Reliability of IUIs;
  • Sustainable AI solutions;
  • Intelligent interactive systems for explainable AI;
  • IoT-assisted computationally intelligent methods for HCI pattern recognition;
  • Cloud computing for HCI pattern recognition;
  • User experience (UX) in IUIs;
  • Intelligent UX;
  • Personalized UX;
  • Accessibility of intelligent HCI;
  • Inclusive intelligent HCI;
  • Usability testing in IUIs;
  • Acceptance and use of IUIs;
  • Optimized models for UX in IUIs;
  • Trust, confidence, reliance, and user privacy in IUIs

Dr. Boštjan Šumak
Dr. Maja Pušnik
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. Sensors 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 2600 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
  • user experience
  • artificial intelligence

Published Papers (2 papers)

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Research

18 pages, 6568 KiB  
Article
Gesture-Based Interactions: Integrating Accelerometer and Gyroscope Sensors in the Use of Mobile Apps
by Sergio Caro-Alvaro, Eva Garcia-Lopez, Alexander Brun-Guajardo, Antonio Garcia-Cabot and Aekaterini Mavri
Sensors 2024, 24(3), 1004; https://doi.org/10.3390/s24031004 - 04 Feb 2024
Viewed by 1047
Abstract
This study investigates the feasibility and functionality of accelerometer and gyroscope sensors for gesture-based interactions in mobile app user experience. The core of this innovative approach lies in introducing a dynamic and intuitive user interaction model with the device sensors. The Android app [...] Read more.
This study investigates the feasibility and functionality of accelerometer and gyroscope sensors for gesture-based interactions in mobile app user experience. The core of this innovative approach lies in introducing a dynamic and intuitive user interaction model with the device sensors. The Android app developed for this purpose has been created for its use in controlled experiments. Methodologically, it was created as a stand-alone tool to both capture quantitative (time, automatically captured) and qualitative (behavior, collected with post-task questionnaires) variables. The app’s setting features a set of modules with two levels each (randomized presentation applied, minimizing potential learning effects), allowing users to interact with both sensor-based and traditional touch-based scenarios. Preliminary results with 22 participants reveal that tasks involving sensor-based interactions tend to take longer to complete when compared to the traditional ones. Remarkably, many participants rated sensor-based interactions as a better option than touch-based interactions, as seen in the post-task questionnaires. This apparent discrepancy between objective completion times and subjective user perceptions requires a future in-depth exploration of factors influencing user experiences, including potential learning curves, cognitive load, and task complexity. This study contributes to the evolving landscape of mobile app user experience, emphasizing the benefits of considering the integration of device sensors (and gesture-based interactions) in common mobile usage. Full article
(This article belongs to the Special Issue Intelligent Human-Computer Interaction Systems and Their Evaluation)
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13 pages, 978 KiB  
Article
KRP-DS: A Knowledge Graph-Based Dialogue System with Inference-Aided Prediction
by Qiang He, Shuobo Xu, Zhenfang Zhu, Peng Wang, Kefeng Li, Quanfeng Zheng and Yanshun Li
Sensors 2023, 23(15), 6805; https://doi.org/10.3390/s23156805 - 30 Jul 2023
Viewed by 1098
Abstract
With the popularity of ChatGPT, there has been increasing attention towards dialogue systems. Researchers are dedicated to designing a knowledgeable model that can engage in conversations like humans. Traditional seq2seq dialogue models often suffer from limited performance and the issue of generating safe [...] Read more.
With the popularity of ChatGPT, there has been increasing attention towards dialogue systems. Researchers are dedicated to designing a knowledgeable model that can engage in conversations like humans. Traditional seq2seq dialogue models often suffer from limited performance and the issue of generating safe responses. In recent years, large-scale pretrained language models have demonstrated their powerful capabilities across various domains. Many studies have leveraged these pretrained models for dialogue tasks to address concerns such as safe response generation. Pretrained models can enhance responses by carrying certain knowledge information after being pre-trained on large-scale data. However, when specific knowledge is required in a particular domain, the model may still generate bland or inappropriate responses, and the interpretability of such models is poor. Therefore, in this paper, we propose the KRP-DS model. We design a knowledge module that incorporates a knowledge graph as external knowledge in the dialogue system. The module utilizes contextual information for path reasoning and guides knowledge prediction. Finally, the predicted knowledge is used to enhance response generation. Experimental results show that our proposed model can effectively improve the quality and diversity of responses while having better interpretability, and outperforms baseline models in both automatic and human evaluations. Full article
(This article belongs to the Special Issue Intelligent Human-Computer Interaction Systems and Their Evaluation)
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