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Electronics
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Human-Computer Interactions in E-health
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Human-Computer Interactions in E-health

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

Deadline for manuscript submissions: 15 September 2024 | Viewed by 3692

Special Issue Editors

Dr. Hyun K. Kim

E-Mail Website
Guest Editor
School of Information Convergence, Kwangwoon University, Seoul 01897, Republic of Korea
Interests: metaverse UX; accessibility andigital inequality; AI UX (artificial intelligence user experience); human–AI interaction
Dr. Sung-Hee Kim

E-Mail Website
Guest Editor
Industrial ICT Engineering, Dong-eui University, Busan 47340, Republic of Korea
Interests: data visualization; human–computer interaction; health informatics; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Jaehyun Park

E-Mail Website
Guest Editor
Department of Industrial & Management Engineering, Incheon National University, Incheon 22012, Republic of Korea
Interests: HCI; UX; UI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This special journal issue is a scientific and multidisciplinary journal that aims to study the healthcare system from the perspective of human–computer interactions. Healthcare services tend to rely more on information technology (IT) and healthcare using IT technology, which can provide high efficiency, accessibility, and quality health services. Therefore, it is essential to develop a healthcare system that can provide a more user-friendly and high-level user experience through the efficient use of IT technology.

The journal covers all healthcare topics studied from the HCI perspective, including e-health, healthcare systems, healthcare services, medical systems, internet healthcare, mobile health, telehealth, health informatics, medical service/systems, rehabilitation and musculoskeletal disease. In addition, it covers various user groups (e.g., patients, medical staff, patients with chronic diseases, the elderly, the disabled, etc.) and various devices (e.g., wearable, computer, mobile devices, robots and exoskeletons).

In detail, this Special Issue includes the following research topics:

- A study evaluating/improving existing healthcare systems from the HCI perspective;

- Research on the development/design of new systems, including the HCI perspective;

- Research on the visualization of data collected from healthcare;

- Robotics research to improve musculoskeletal disorders;

- Review articles related to e-health.

In addition to this, if it is a healthcare study using digital technology, all topics are welcome.

Dr. Hyun K. Kim
Dr. Sung-Hee Kim
Dr. Jaehyun Park
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

  • e-health
  • healthcare system
  • healthcare service
  • medical system
  • internet health care
  • mobile health
  • telehealth
  • health informatics
  • medical service/system
  • musculoskeletal disease

Published Papers (5 papers)

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Research

20 pages, 1186 KiB  
Article
Reengineering eADVICE for Long Waitlists: A Tale of Two Systems and Conditions
by Deborah Richards, Patrina H. Y. Caldwell, Amal Abdulrahman, Amy von Huben, Karen Waters and Karen M. Scott
Electronics 2024, 13(14), 2785; https://doi.org/10.3390/electronics13142785 - 16 Jul 2024
Viewed by 226
Abstract
Long outpatient waiting times pose a significant global challenge in healthcare, impacting children and families with implications for health outcomes. This paper presents the eHealth system called eADVICE (electronic Advice and Diagnosis Via the Internet following Computerised Evaluation) that is designed to address [...] Read more.
Long outpatient waiting times pose a significant global challenge in healthcare, impacting children and families with implications for health outcomes. This paper presents the eHealth system called eADVICE (electronic Advice and Diagnosis Via the Internet following Computerised Evaluation) that is designed to address waiting list challenges for paediatricians. Initially designed for children’s incontinence, the system’s success in terms of health goals and user experience led to its adaptation for paediatric sleep problems. This paper focuses on user experiences and the development of a working alliance with the virtual doctor, alongside health outcomes based on a randomised controlled trial (N = 239) for incontinence. When reengineering eADVICE to sleep disorders, the promising results regarding the reciprocal relationship between user experience and building a working alliance encouraged a focus on the further development of the embodied conversational agent (ECA) component. This involved tailoring the ECA discussion to patient cognition (i.e., beliefs and goals) to further improve engagement and outcomes. The proposed eADVICE framework facilitates adaptation across paediatric conditions, offering a scalable model to enhance access and self-efficacy during care delays. Full article
(This article belongs to the Special Issue Human-Computer Interactions in E-health)
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33 pages, 4931 KiB  
Article
A Method for Ocular Disease Diagnosis through Visual Prediction Explainability
by Antonella Santone, Mario Cesarelli, Emanuella Colasuonno, Vitoantonio Bevilacqua and Francesco Mercaldo
Electronics 2024, 13(14), 2706; https://doi.org/10.3390/electronics13142706 - 10 Jul 2024
Viewed by 347
Abstract
Ocular diseases can range in severity, with some being more serious than others. As a matter of fact, there are several common and severe eye diseases, for instance, glaucoma, i.e., a group of eye conditions that damage the optic nerve, often associated with [...] Read more.
Ocular diseases can range in severity, with some being more serious than others. As a matter of fact, there are several common and severe eye diseases, for instance, glaucoma, i.e., a group of eye conditions that damage the optic nerve, often associated with elevated intraocular pressure. Effective management and prevention strategies require a multifaceted approach, involving healthcare providers, public health officials and community education. Regular screenings and early interventions are crucial in reducing the impact of eye diseases on individuals and populations. In this paper, we propose a method aimed to detect the presence of ocular disease from the automatic analysis of eye fundus photographs. We consider deep learning; in detail, we adopt several convolutional neural networks aimed to train several models to be able to discriminate between different eye diseases. Furthermore, to boost the application of deep learning in real-world everyday medical practice, we adopt a method to understand which areas of the images are of interest from the model’s point of view; this allows us to detect disease by providing in this way disease localization by explainability. In the experimental analysis, we provide a set of four different experiments: in the first one, we propose a model to discern between age-related macular degenerations and normal fundus (obtaining an accuracy of 0.91); in the second one, the model is able to discriminate between cataract and normal fundus (obtaining an accuracy of 0.92); the third experiment is related to a model aimed to discriminate between glaucoma and normal ocular fundus (obtaining an accuracy of 0.88); and the last experiment is related to a model aimed to discern between pathological myopia and normal ocular fundus (obtaining an accuracy of 0.95). Thus, the experimental analysis confirms the effectiveness of the proposed method from a quantitative point of view (analysis aimed at understanding whether the model is able to correctly identify the disease) but also from a qualitative one, with a detailed and reasoned analysis aimed at understanding whether the model is able to correctly localize the disease. Full article
(This article belongs to the Special Issue Human-Computer Interactions in E-health)
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13 pages, 579 KiB  
Article
Evaluating Deep Learning Resilience in Retinal Fundus Classification with Generative Adversarial Networks Generated Images
by Marcello Di Giammarco, Antonella Santone, Mario Cesarelli, Fabio Martinelli and Francesco Mercaldo
Electronics 2024, 13(13), 2631; https://doi.org/10.3390/electronics13132631 - 4 Jul 2024
Viewed by 297
Abstract
The evaluation of Generative Adversarial Networks in the medical domain has shown significant potential for various applications, including adversarial machine learning on medical imaging. This study specifically focuses on assessing the resilience of Convolutional Neural Networks in differentiating between real and Generative Adversarial [...] Read more.
The evaluation of Generative Adversarial Networks in the medical domain has shown significant potential for various applications, including adversarial machine learning on medical imaging. This study specifically focuses on assessing the resilience of Convolutional Neural Networks in differentiating between real and Generative Adversarial Network-generated retinal images. The main contributions of this research include the training and testing of Convolutional Neural Networks to evaluate their ability to distinguish real images from synthetic ones. By identifying networks with optimal performances, the study ensures the development of better models for diagnostic classification, enhancing generalization and resilience to adversarial images. Overall, the aim of the study is to demonstrate that the application of Generative Adversarial Networks can improve the resilience of the tested networks, resulting in better classifiers for retinal images. In particular, a network developed by authors, i.e., Standard_CNN, reports the best performance with accuracy equal to 1. Full article
(This article belongs to the Special Issue Human-Computer Interactions in E-health)
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22 pages, 6722 KiB  
Article
A Cloud-Based WEB Platform for Fall Risk Assessment Using a Therapist-Centered User Interface Which Enables Patients’ Tracking Remotely
by Jesús Damián Blasco-García, Nieves Pavón-Pulido, Juan Antonio López-Riquelme, Ana María Roldán-Ruiz and Jorge Juan Feliu-Batlle
Electronics 2024, 13(11), 2220; https://doi.org/10.3390/electronics13112220 - 6 Jun 2024
Viewed by 420
Abstract
This work describes a system to help in the remote assessment of fall risk in elderly people. A portable hardware system equipped with an RGB-D sensor is used for motion capture. A set of anonymous frames, representing the process of skeleton tracking, and [...] Read more.
This work describes a system to help in the remote assessment of fall risk in elderly people. A portable hardware system equipped with an RGB-D sensor is used for motion capture. A set of anonymous frames, representing the process of skeleton tracking, and a collection of sequences of interesting features, obtained from body landmark evaluations through time, are stored in the Cloud for each patient. A WEB dashboard allows for tailored tests to be designed, which include the typical items within well-known fall risk evaluation tests in the literature. Such a dashboard helps therapists to evaluate each item from the analysis and observation of the sequences and the 3D representation of the body through time, and to compare the results of tests carried out in different moments, checking on the evolution of the fall risk. The software architecture that implements the system allows the information to be stored in a safe manner and preserves patients’ privacy. The paper shows the obtained results after testing an early prototype of the system, a discussion about its advantages, and the current limitations from the Human–Computer Interaction point of view, and a plan to deploy and evaluate the system from the usability perspective in the near future. Full article
(This article belongs to the Special Issue Human-Computer Interactions in E-health)
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18 pages, 2334 KiB  
Article
How to Design and Evaluate mHealth Apps? A Case Study of a Mobile Personal Health Record App
by Guyeop Kim, Dongwook Hwang, Jaehyun Park, Hyun K. Kim and Eui-Seok Hwang
Electronics 2024, 13(1), 213; https://doi.org/10.3390/electronics13010213 - 3 Jan 2024
Cited by 1 | Viewed by 1819
Abstract
The rapid growth of the mHealth market has led to the development of several tools to evaluate user experience. However, there is a lack of universal tools specifically designed for this emerging technology. This study was conducted with the aim of developing and [...] Read more.
The rapid growth of the mHealth market has led to the development of several tools to evaluate user experience. However, there is a lack of universal tools specifically designed for this emerging technology. This study was conducted with the aim of developing and verifying a user experience evaluation scale for mHealth apps based on factors proposed in previous research. The initial draft of the tool was created following a comprehensive review of existing questionnaires related to mHealth app evaluation. The validity of this scale was then tested through exploratory and confirmatory factor analysis. The results of the factor analysis led to the derivation of 16 items, which were conceptually mapped to five factors: ease of use and satisfaction, information architecture, usefulness, ease of information, and aesthetics. A case study was also conducted to improve mHealth apps concerning personal health records using this scale. In conclusion, the developed user experience evaluation scale for mHealth apps can provide comprehensive user feedback and contribute to the improvement of these apps. Full article
(This article belongs to the Special Issue Human-Computer Interactions in E-health)
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