A Novel User Interface Design for Enhancing Accessibility in Mobile Applications

Main Article Content

Md. Saad Amin
Primitiva Morales-Romero
Miguel Chamorro-Atalaya
M.Bhavsingh

Abstract

Mobile applications have revolutionized the way we interact with digital content, offering a myriad of services to users across the globe. However, in this ever-evolving landscape of technology, it is imperative to ensure that mobile applications are not just innovative but inclusive as well. Accessibility, particularly in the context of mobile applications, remains a fundamental concern. This research endeavors to bridge the accessibility gap by proposing a novel approach that leverages machine learning, specifically Convolutional Neural Networks (CNNs), to assess and enhance the inclusivity of mobile application interfaces. In the realm of accessibility, conventional manual assessment methods prove time-consuming and subjective, hindering the timely evaluation of the ever-changing design trends in mobile applications. Our research aims to automate accessibility assessments, providing real-time feedback to developers and designers. This approach not only reduces the reliance on labor-intensive manual assessments but also ensures the inclusivity and user-friendliness of mobile applications, thereby improving the user experience for individuals with diverse needs and abilities. Key contributions of this research include an automated accessibility assessment model, real-time assessment capabilities, enhancement recommendations, and the application of CNNs to identify accessibility-related features. Through this work, we aspire to promote inclusivity, foster a more user-friendly digital environment, and bridge the gap between accessible design and practical application in the realm of mobile applications..

Article Details

How to Cite
[1]
Md. Saad Amin, Primitiva Morales-Romero, Miguel Chamorro-Atalaya, and M.Bhavsingh, “A Novel User Interface Design for Enhancing Accessibility in Mobile Applications”, Int. J. Comput. Eng. Res. Trends, vol. 10, no. 8, pp. 26–33, Aug. 2023.
Section
Research Articles

References

Urry, J. (2012). Social networks, mobile lives and social inequalities. Journal of transport geography, 21, 24-30.

Rutter, R., Lauke, P. H., Waddell, C., Thatcher, J., Henry, S. L., Lawson, B., ... & Urban, M. (2007). Web accessibility: Web standards and regulatory compliance. Apress.

Brinck, T., Gergle, D., & Wood, S. D. (2001). Usability for the web: Designing web sites that work. Elsevier.

Al-Hmouz, A., Shen, J., & Yan, J. (2009). A machine learning based framework for adaptive mobile learning. In Advances in Web Based Learning–ICWL 2009: 8th International Conference, Aachen, Germany, August 19-21, 2009. Proceedings 8 (pp. 34-43). Springer Berlin Heidelberg.

Bharadiya, J. (2023). Convolutional Neural Networks for Image Classification. International Journal of Innovative Science and Research Technology, 8(5), 673-677.

Al-Razgan, M., Almoaiqel, S., Alrajhi, N., Alhumegani, A., Alshehri, A., Alnefaie, B., ... & Rushdi, S. (2021). A systematic literature review on the usability of mobile applications for visually impaired users. PeerJ Computer Science, 7, e771.

Di Gregorio, M., Di Nucci, D., Palomba, F., & Vitiello, G. (2022). The making of accessible android applications: an empirical study on the state of the practice. Empirical Software Engineering, 27(6), 145.

Djamasbi, S., McAuliffe, D., Gomez, W., Kardzhaliyski, G., Liu, W., & Oglesby, F. (2014). Designing for success: Creating business value with mobile user experience (UX). In HCI in Business: First International Conference, HCIB 2014, Held as Part of HCI International 2014, Heraklion, Crete, Greece, June 22-27, 2014. Proceedings 1 (pp. 299-306). Springer International Publishing.

Yan, S., & Ramachandran, P. G. (2019). The current status of accessibility in mobile apps. ACM Transactions on Accessible Computing (TACCESS), 12(1), 1-31.

Gazzawe, F., Mayouf, M., Lock, R., & Alturki, R. (2022). The Role of Machine Learning in E-Learning Using the Web and AI-Enabled Mobile Applications. Mobile Information Systems, 2022.

Baumann, R., Schwartz, J. K., & Smith, R. O. (2014). Creating accessible mobile applications: a case study of challenges and lessons. In Rehabilitation Engineering and Assistive Technology Society Annual Conference RESNA.

Kaluarachchi, T., Reis, A., & Nanayakkara, S. (2021). A review of recent deep learning approaches in human-centered machine learning. Sensors, 21(7), 2514.

Ech-Cherif, A., Misbhauddin, M., & Ech-Cherif, M. (2019, May). Deep neural network based mobile dermoscopy application for triaging skin cancer detection. In 2019 2nd international conference on computer applications & information security (ICCAIS) (pp. 1-6). IEEE.

Heinrich, K., Zschech, P., Janiesch, C., & Bonin, M. (2021). Process data properties matter: Introducing gated convolutional neural networks (GCNN) and key-value-predict attention networks (KVP) for next event prediction with deep learning. Decision Support Systems, 143, 113494.