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Electronics
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Assistive Technology: Advances, Applications and Challenges
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Assistive Technology: Advances, Applications and Challenges

A special issue of Electronics (ISSN 2079-9292).

Deadline for manuscript submissions: 15 June 2026 | Viewed by 7071

Special Issue Editors

Dr. Paul Whittington

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Guest Editor
Department of Computing & Informatics, Faculty of Science & Technology, Bournemouth University, Poole BH12 5BB, UK
Interests: assistive technology; human factors; usability engineering
Prof. Dr. Huseyin Dogan

E-Mail Website
Guest Editor
Department of Computing & Informatics, Faculty of Science & Technology, Bournemouth University, Poole BH12 5BB, UK
Interests: human factors; assistive technology; usability engineering; systems of systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Assistive technology (AT) is designed to increase, maintain, or improve the functioning of people with disabilities. These products include hardware, software, or mechanical devices, such as wheelchairs. The purpose of AT is to improve the quality of life of people with a wide range of disabilities. The user community can experience varying levels of ability and it can be challenging to develop a single AT solution to suit the needs of all users.

There is an estimated 1.3 billion people (16% of the global population) who have significant disabilities that affect their daily lives and over 2.6 billion people require one or more assistive products. The World Health Organization (WHO) has developed the International Classification for Disability, Functioning and Health (ICF) framework, which has become the worldwide standard for measuring health and disability. There is an extensive range of AT products available to assist people with disabilities and improve their quality of life; however, awareness is a challenge and the WHO has identified that 1 in 10 people worldwide do not have access to required AT products.

AT can be used in education, to ensure that pupils have equal opportunities to access teaching materials. Previous studies by the Commonwealth of Learning have reported limited online educational resources relating to AT for teachers and support staff who assist pupils with disabilities.

It has been highlighted that education should be inclusive for all pupils and reasonable adjustments should be made based on the needs of an individual.

This Special Issue focuses on publications that provide insights into AT research studies, to understand methods being adopted and to address the current challenges in this domain. This can also include lessons learned from practical applications of AT to the specific needs of individuals, as well as assessing their social acceptability of solutions. Publications that investigate the impact of AI-supported AT are also welcome.

Original research articles for this Special Issue may include, but are not limited to, the following topics:

  • Accessibility (accessible authentication, accessible design, usable accessibility, user experience, visual design, and web accessibility).
  • AI-enabled AT and issues (AI-assisted learning/communication, fairness, trust, and privacy).
  • Diversity (technology acceptance, technology adoption, and technology discrimination).
  • Human-centered design approaches (design solutions and evaluations, heuristics, multimodal interactions, and participatory design).
  • Inclusivity (affordability, availability, design for all, inclusive by design, and universal design).
  • Application areas (aging population, ambient assisted living, mobility, smart systems, telecare, and telehealth).
  • Industrial case studies (education and training programmes, healthcare).

This Special Issue will supplement existing literature in domains relating to the development of AT solutions to improve quality of life. The content will increase AT knowledge, both for users and individuals providing support for people with disabilities. Additionally, the Special Issue has the potential to publish literature to improve users’ acceptance and experience of AI-enabled AT. This will minimize negative impacts that may result from the deployment and use of this type of technology.

We look forward to receiving your contributions.

Dr. Paul Whittington
Prof. Dr. Huseyin Dogan
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 250 words) can be sent to the Editorial Office for assessment.

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

  • accessibility
  • artificial intelligence
  • assistive technology
  • disability
  • education technology
  • human-centred design
  • inclusivity
  • telehealth
  • usability

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Published Papers (5 papers)

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Research

13 pages, 712 KB  
Article
EduAbility: A Usability Evaluation of an Educational Recommendation and Training Tool for Pupils with Disabilities to Promote Inclusivity
by Paul Whittington, Huseyin Dogan and Chinduji Emereole
Electronics 2026, 15(5), 970; https://doi.org/10.3390/electronics15050970 - 27 Feb 2026
Viewed by 384
Abstract
This paper presents a usability evaluation of EduAbility, an Android application that supports inclusivity through assistive technology recommendations and training to pupils with disabilities, teachers, teaching assistants and parents/carers. EduAbility consists of a Recommendation System and Training Package that increases awareness of assistive [...] Read more.
This paper presents a usability evaluation of EduAbility, an Android application that supports inclusivity through assistive technology recommendations and training to pupils with disabilities, teachers, teaching assistants and parents/carers. EduAbility consists of a Recommendation System and Training Package that increases awareness of assistive technology (AT) to improve quality of life for individuals with disabilities. Following a previous usability evaluation at a higher education institution, EduAbility was subsequently developed from the qualitative feedback. A further evaluation at a secondary school and college (n = 9) are presented, where System Usability Scale (SUS) and NASA Task Load Index (TLX) quantitatively measure usability and workload, with Think Aloud providing qualitative data. The results highlight the significant potential of EduAbility to educate teachers, teaching assistants and parents/carers on AT, with the product recommendations being particularly valuable to increase awareness. Suggestions for future work are also discussed as well as the wider impacts of EduAbility on promoting the use of AT in education. Full article
(This article belongs to the Special Issue Assistive Technology: Advances, Applications and Challenges)
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27 pages, 2099 KB  
Article
Brain Tumor Classification Using DINO Features and Lightweight Classifiers
by Rim Missaoui, Marco Del Coco, Wajdi Saadaoui, Wided Hechkel, Abdelhamid Helali, Pierluigi Carcagnì and Marco Leo
Electronics 2026, 15(5), 952; https://doi.org/10.3390/electronics15050952 - 26 Feb 2026
Viewed by 843
Abstract
The accurate detection and classification of brain tumors from magnetic resonance imaging (MRI) are critical for diagnosis and treatment planning. While deep learning has shown remarkable success in this domain, many state-of-the-art models rely on complex, end-to-end convolutional neural networks (CNNs) that require [...] Read more.
The accurate detection and classification of brain tumors from magnetic resonance imaging (MRI) are critical for diagnosis and treatment planning. While deep learning has shown remarkable success in this domain, many state-of-the-art models rely on complex, end-to-end convolutional neural networks (CNNs) that require extensive computational resources and large, annotated datasets for training. This work proposes a novel and efficient methodology that, for the first time, leverages self-supervised DINO vision transformer backbones (DINO v1, DINOv2, and DINOv3) on a large corpus of natural images as powerful feature extractors for brain tumor analysis. We utilize the rich, general-purpose features from DINO-family backbones without fine-tuning the core model. These extracted features are then fed into a simpler, task-specific classifier (such as a support vector machine or a multi-layer perceptron) for the final detection and multi-class classification (e.g., glioma, meningioma, and pituitary tumor). Our methodology is evaluated on two benchmark medical imaging datasets with various classifying granularities. The results demonstrate that the proposed method achieves competitive and, in some cases, superior classification accuracy compared to representative fine-tuned convolutional neural networks and attention-based architectures, while significantly reducing the number of trainable parameters and training time. In particular, the best configuration achieves up to 98.17% accuracy and an F1-score of 98.18% on the 15-class dataset and 99.08% accuracy and an F1-score of 99.02% on the 4-class dataset. This study confirms the exceptional transfer learning capabilities of self-supervised vision transformers like DINO in the medical imaging domain, establishing it as a highly effective and efficient backbone for robust brain tumor detection and classification systems. Full article
(This article belongs to the Special Issue Assistive Technology: Advances, Applications and Challenges)
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14 pages, 638 KB  
Article
A Low-Cost Head-Controlled and Sip-and-Puff Mouse: System Design and Preliminary Findings
by Rodrigo Duarte, Nuno Vieira Lopes and Paulo Jorge Coelho
Electronics 2025, 14(24), 4953; https://doi.org/10.3390/electronics14244953 - 17 Dec 2025
Viewed by 897
Abstract
This work introduces a low-cost, wearable assistive mouse designed to support digital interaction for individuals with motor impairments. The system combines inertial sensing for head-movement tracking and a pressure-based interface for simulating mouse clicks via “sip-and-puff” actions. The device enables full mouse control [...] Read more.
This work introduces a low-cost, wearable assistive mouse designed to support digital interaction for individuals with motor impairments. The system combines inertial sensing for head-movement tracking and a pressure-based interface for simulating mouse clicks via “sip-and-puff” actions. The device enables full mouse control (pointer movement, clicks, and double-clicks) without relying on hand mobility. Preliminary evaluations, conducted with input from occupational therapy professionals, demonstrated promising usability and functionality comparable to commercial devices. The proposed solution offers a cost-effective, open-source alternative to existing adaptive technologies, with future development aimed at broader testing and integration in rehabilitation settings. Future work will include usability testing with individuals presenting real motor impairments to validate clinical applicability. Full article
(This article belongs to the Special Issue Assistive Technology: Advances, Applications and Challenges)
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21 pages, 1280 KB  
Article
AI-Assistive Technology Adoption and Mental Health Disorders in Visually Impaired University Students
by Ibrahim A. Elshaer, Sameer Mos Alnajdi and Mostafa Aboulnour Salem
Electronics 2025, 14(20), 4036; https://doi.org/10.3390/electronics14204036 - 14 Oct 2025
Cited by 6 | Viewed by 2018
Abstract
The rapid integration of Artificial Intelligence Assistive Technology (AIAT) into higher education has generated new avenues for visually impaired university students, primarily in enhancing accessibility, self-autonomy, and academic performance. This study examined associations between AIAT-related perceptions and mental-health indicators (depression, anxiety, and stress) [...] Read more.
The rapid integration of Artificial Intelligence Assistive Technology (AIAT) into higher education has generated new avenues for visually impaired university students, primarily in enhancing accessibility, self-autonomy, and academic performance. This study examined associations between AIAT-related perceptions and mental-health indicators (depression, anxiety, and stress) among visually impaired higher education students in the Kingdom of Saudi Arabia (KSA). A quantitative research approach was employed, using a self-administrated questionnaire targeting 390 visually impaired students in KSA universities. Partial least squares structural equation modelling (PLS-SEM) was employed as the main data analysis technique. The findings emphasised two important issues. First, performance expectancy (PE) of AIAT adoption, Effort expectancy (EE), and social influence (SI) are forceful psychological facilitators that can buffer against the feeling of depression and anxiety in visually impaired university students. Second, minimising the feeling of stress requires more than the existence of good infrastructure or social support; it necessitates systemic and ongoing interventions, comprising proactive university support, an accessible learning context, and personalised training programmes. These insights highlight the need for implementing inclusive support systems that combine technological, psychological, and university dimensions to promote the advantages of AIAT adoption for visually impaired students. Full article
(This article belongs to the Special Issue Assistive Technology: Advances, Applications and Challenges)
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31 pages, 927 KB  
Article
A Narrative Review on Key Values Indicators of Millimeter Wave Radars for Ambient Assisted Living
by Maria Gardano, Antonio Nocera, Michela Raimondi, Linda Senigagliesi and Ennio Gambi
Electronics 2025, 14(13), 2664; https://doi.org/10.3390/electronics14132664 - 30 Jun 2025
Cited by 3 | Viewed by 2020
Abstract
The demographic shift toward an aging population calls for innovative strategies to ensure independence, health, and quality of life in later years. In this context, Ambient Assisted Living (AAL) solutions, supported by Information and Communication Technologies (ICTs), offer promising advances for non-invasive and [...] Read more.
The demographic shift toward an aging population calls for innovative strategies to ensure independence, health, and quality of life in later years. In this context, Ambient Assisted Living (AAL) solutions, supported by Information and Communication Technologies (ICTs), offer promising advances for non-invasive and continuous support. Commonly, ICTs are evaluated only from the perspectives related to key performance indicators (KPIs); nevertheless, the design and implementation of such technologies must account for important psychological, social, and ethical dimensions. Radar-based sensing systems are emerging as an option due to their unobtrusive nature and capacity to operate without direct user interaction. This work explores how radar technologies, particularly those operating in the millimeter wave (mmWave) spectrum, can provide core key value indicators (KVIs) essential to aging societies, such as human dignity, trustworthiness, fairness, and sustainability. Through a review of key application domains, the paper illustrates the practical contributions of mmWave radar in Ambient Assisting Living (AAL) contexts, underlining how its technical attributes align with the complex needs of elderly care environments and produce value for society. This work uniquely integrates key value indicator (KVI) frameworks with mmWave radar capabilities to address unmet ethical needs in the AAL domain. It advances existing literature by proposing a value-driven design approach that directly informs technical specifications, enabling the alignment of engineering choices with socially relevant values and supporting the development of technologies for a more inclusive and ethical society. Full article
(This article belongs to the Special Issue Assistive Technology: Advances, Applications and Challenges)
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