Designing for Inclusivity: Enhancing User Experience in Mobile App Development
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Abstract
This research paper explores the critical aspect of designing for inclusivity in the context of mobile app development. In an increasingly digital world, ensuring that mobile applications are accessible to users of all abilities is of paramount importance. This study delves into the methods and principles of creating mobile apps that provide a seamless and equitable user experience for individuals with diverse abilities and needs. It investigates the application of universal design principles, adaptive interfaces, and assistive technologies in enhancing accessibility. The paper also examines case studies and best practices from real-world mobile app development projects, demonstrating the tangible benefits of inclusive design. By addressing the challenges and opportunities in this area, the paper aims to contribute to the advancement of more inclusive and user-centric mobile app design practices.
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References
Whig, P. (2019a). A Novel Multi-Center and Threshold Ternary Pattern. International Journal of Machine Learning for Sustainable Development, 1(2), 1–10.
Whig, P. (2019d). Exploration of Viral Diseases mortality risk using machine learning. International Journal of Machine Learning for Sustainable Development, 1(1), 11–20.
Suryadevara, Chaitanya Krishna, Unveiling Urban Mobility Patterns: A Comprehensive Analysis of Uber (December 21, 2019). International Journal of Engineering, Science and Mathematics, Vol. 8 Issue 12, December 2019, Available at SSRN: https://ssrn.com/abstract=4591998
Chaitanya Krishna Suryadevara. (2019). A NEW WAY OF PREDICTING THE LOAN APPROVAL PROCESS USING ML TECHNIQUES. International Journal of Innovations in Engineering Research and Technology, 6(12), 38–48. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3654
Johnson, M. J., & Smith, A. B. (2019). "Immersive User Experience in Virtual Reality: Design Principles and Applications." Journal of Interactive Technology, 3(2), 45-58.
Chen, L., & Wang, Q. (2019). "Enhancing Interaction in Virtual Reality through Haptic Feedback." International Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2019, Los Angeles, CA, USA.
Smith, R. H., & Patel, S. (2019). "Spatial Audio Design for Virtual Reality: A User-Centered Approach." ACM Transactions on Computer-Human Interaction, 26(3), 1-18.
Lee, J. S., & Kim, E. (2019). "Eye-Tracking Techniques for VR User Experience Assessment." IEEE Transactions on Visualization and Computer Graphics, 25(5), 1988-2001.
Zhang, H., & Chen, Y. (2019). "Ergonomics and Comfort in Virtual Reality Head-Mounted Displays." Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 63(1), 1419-1423.
Chaitanya Krishna Suryadevara. (2020). GENERATING FREE IMAGES WITH OPENAI’S GENERATIVE MODELS. International Journal of Innovations in Engineering Research and Technology, 7(3), 49–56. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3653
Chaitanya Krishna Suryadevara. (2020). REAL-TIME FACE MASK DETECTION WITH COMPUTER VISION AND DEEP LEARNING: English. International Journal of Innovations in Engineering Research and Technology, 7(12), 254–259. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3184
Nadikattu, R. R., Mohammad, S. M., & Whig, P. (2020b). Novel economical social distancing smart device for covid-19. International Journal of Electrical Engineering and Technology (IJEET).
Chaitanya Krishna Suryadevara. (2021). ENHANCING SAFETY: FACE MASK DETECTION USING COMPUTER VISION AND DEEP LEARNING. International Journal of Innovations in Engineering Research and Technology, 8(08), 224–229. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3672