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Applied Sciences
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Explainable Artificial Intelligence for Visualization in Human Computer Interactions
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Explainable Artificial Intelligence for Visualization in Human Computer Interactions

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

Deadline for manuscript submissions: closed (20 February 2025) | Viewed by 4359

Special Issue Editor

Dr. Gerasimos Arvanitis

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Patras, 26504 Patras, Greece
Interests: computers and graphics

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the advancements and applications of explainable artificial intelligence (XAI) in the context of visualization for human–computer interactions (HCI). The focus is on research that investigates techniques, methodologies, and frameworks for developing interpretable and explainable AI systems in order to enhance the usability, transparency, and trustworthiness of visualizations in HCI. The Special Issue welcomes original research articles, reviews, and case studies that contribute to the understanding and development of XAI methods for visualization in HCI.

  • Explainable artificial intelligence (XAI);
  • Visualization;
  • Human–computer interactions;
  • Trustworthiness;
  • Interpretable AI;
  • User-centered design;
  • Cognitive computing;
  • Human factors;
  • Visual analytics;
  • Explainability techniques;
  • Explainable machine learning;
  • User experience.

Dr. Gerasimos Arvanitis
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 interactions
  • explainability techniques
  • artificial intelligence

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Published Papers (4 papers)

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Research

23 pages, 2820 KiB  
Article
The AI of Oz: A Conceptual Framework for Democratizing Generative AI in Live-Prototyping User Studies
by Jose Maria Santiago III, Moritz Sendner, David Ralser and Alexander Meschtscherjakov
Appl. Sci. 2025, 15(10), 5506; https://doi.org/10.3390/app15105506 - 14 May 2025
Viewed by 255
Abstract
Commonly used methods in User-Centered Design (UCD) can face challenges in incorporating user feedback during early design stages, often resulting in extended iteration cycles. To address this, we explore the following question: “How can generative artificial intelligence (AI) be utilized to enable prototyping [...] Read more.
Commonly used methods in User-Centered Design (UCD) can face challenges in incorporating user feedback during early design stages, often resulting in extended iteration cycles. To address this, we explore the following question: “How can generative artificial intelligence (AI) be utilized to enable prototyping within user studies to facilitate immediate user feedback integration and validation?” We introduce a conceptual framework for live-prototyping, where designers modify AI-generated components of a prototype in real time through a separate control interface during user testing. This approach invites more immediate interaction between feedback and design decisions. To explore our concept, we engaged in a case study with experienced prototyping practitioners, examining how real-time prototyping might shape design processes. Participants highlighted the framework’s potential to support spontaneous insight generation and enhance collaborative dynamics. However, they also highlighted important considerations, including the need for a certain level of AI knowledge and challenges around planning and reliability. By integrating generative AI into the UCD process, our conceptual framework contributes to ongoing conversations around evolving user-centered methodologies. Full article
(This article belongs to the Special Issue Explainable Artificial Intelligence for Visualization in Human Computer Interactions)
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17 pages, 9355 KiB  
Article
Grasp Pattern Recognition Using Surface Electromyography Signals and Bayesian-Optimized Support Vector Machines for Low-Cost Hand Prostheses
by Alessandro Grattarola, Marta C. Mora, Joaquín Cerdá-Boluda and José V. García Ortiz
Appl. Sci. 2025, 15(3), 1062; https://doi.org/10.3390/app15031062 - 22 Jan 2025
Cited by 1 | Viewed by 1141
Abstract
Every year, thousands of people undergo amputations due to trauma or medical conditions. The loss of an upper limb, in particular, has profound physical and psychological consequences for patients. One potential solution is the use of externally powered prostheses equipped with motorized artificial [...] Read more.
Every year, thousands of people undergo amputations due to trauma or medical conditions. The loss of an upper limb, in particular, has profound physical and psychological consequences for patients. One potential solution is the use of externally powered prostheses equipped with motorized artificial hands. However, these commercially available prosthetic hands are prohibitively expensive for most users. In recent years, advancements in 3D printing and sensor technologies have enabled the design and production of low-cost, externally powered prostheses. This paper presents a pattern-recognition-based human–prosthesis interface that utilizes surface electromyography (sEMG) signals, captured by an affordable device, the Myo armband. A Support Vector Machine (SVM) algorithm, optimized using Bayesian techniques, is trained to classify the user’s intended grasp from among nine common grasping postures essential for daily life activities and functional prosthetic performance. The proposal is viable for real-time implementations on low-cost platforms with 85% accuracy in grasping posture recognition. Full article
(This article belongs to the Special Issue Explainable Artificial Intelligence for Visualization in Human Computer Interactions)
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24 pages, 5164 KiB  
Article
Contextual Background Estimation for Explainable AI in Temperature Prediction
by Bartosz Szostak, Rafal Doroz and Magdalena Marker
Appl. Sci. 2025, 15(3), 1057; https://doi.org/10.3390/app15031057 - 22 Jan 2025
Cited by 1 | Viewed by 1006
Abstract
Accurate weather prediction and electrical load modeling are critical for optimizing energy systems and mitigating environmental impacts. This study explores the integration of the novel Mean Background Method and Background Estimation Method with Explainable Artificial Intelligence (XAI) with the aim to enhance the [...] Read more.
Accurate weather prediction and electrical load modeling are critical for optimizing energy systems and mitigating environmental impacts. This study explores the integration of the novel Mean Background Method and Background Estimation Method with Explainable Artificial Intelligence (XAI) with the aim to enhance the evaluation and understanding of time-series models in these domains. The electrical load or temperature predictions are regression-based problems. Some XAI methods, such as SHAP, require using the base value of the model as the background to provide an explanation. However, in contextualized situations, the default base value is not always the best choice. The selection of the background can significantly affect the corresponding Shapley values. This paper presents two innovative XAI methods designed to provide robust context-aware explanations for regression and time-series problems, addressing critical gaps in model interpretability. They can be used to improve background selection to make more conscious decisions and improve the understanding of predictions made by models that use time-series data. Full article
(This article belongs to the Special Issue Explainable Artificial Intelligence for Visualization in Human Computer Interactions)
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18 pages, 1726 KiB  
Article
Explainable AI-Enhanced Human Activity Recognition for Human–Robot Collaboration in Agriculture
by Lefteris Benos, Dimitrios Tsaopoulos, Aristotelis C. Tagarakis, Dimitrios Kateris, Patrizia Busato and Dionysis Bochtis
Appl. Sci. 2025, 15(2), 650; https://doi.org/10.3390/app15020650 - 10 Jan 2025
Cited by 3 | Viewed by 1426
Abstract
This study addresses a critical gap in human activity recognition (HAR) research by enhancing both the explainability and efficiency of activity classification in collaborative human–robot systems, particularly in agricultural environments. While traditional HAR models often prioritize improving overall classification accuracy, they typically lack [...] Read more.
This study addresses a critical gap in human activity recognition (HAR) research by enhancing both the explainability and efficiency of activity classification in collaborative human–robot systems, particularly in agricultural environments. While traditional HAR models often prioritize improving overall classification accuracy, they typically lack transparency in how sensor data contribute to decision-making. To fill this gap, this study integrates explainable artificial intelligence, specifically SHapley Additive exPlanations (SHAP), thus enhancing the interpretability of the model. Data were collected from 20 participants who wore five inertial measurement units (IMUs) at various body positions while performing material handling tasks involving an unmanned ground vehicle in a field collaborative harvesting scenario. The results highlight the central role of torso-mounted sensors, particularly in the lumbar region, cervix, and chest, in capturing core movements, while wrist sensors provided useful complementary information, especially for load-related activities. The XGBoost-based model, selected mainly for allowing an in-depth analysis of feature contributions by considerably reducing the complexity of calculations, demonstrated strong performance in HAR. The findings indicate that future research should focus on enlarging the dataset, investigating the use of additional sensors and sensor placements, and performing real-world trials to enhance the model’s generalizability and adaptability for practical agricultural applications. Full article
(This article belongs to the Special Issue Explainable Artificial Intelligence for Visualization in Human Computer Interactions)
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