AI-Driven Approaches to Improve Accessibility Testing Across IoT Devices
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© 2024 by IJCTT Journal
Volume-72 Issue-9
Year of Publication : 2024
Authors : Saurabh Kapoor
DOI :  10.14445/22312803/IJCTT-V72I9P127

How to Cite?

Saurabh Kapoor, "AI-Driven Approaches to Improve Accessibility Testing Across IoT Devices ," International Journal of Computer Trends and Technology, vol. 72, no. 9, pp. 170-175, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I9P127

Abstract
As IoT-enabled devices and smart technologies become increasingly integrated into daily life, ensuring accessibility for all users, including those with disabilities, is critical. Traditional accessibility testing methods, such as manual testing and automated tools, have scope, accuracy, and scalability limitations, especially for dynamic and context-aware IoT environments. This research paper proposes a novel AI-driven approach to automated accessibility testing that leverages machine learning, Natural Language Processing (NLP), and computer vision techniques to identify and predict complex accessibility issues in real time for IoT devices. By learning from real user interactions and continuously updating its knowledge base, this AI-powered system can provide more context-aware and comprehensive accessibility assessments for connected devices, enhancing user usability. The proposed approach aims to enhance accessibility testing by improving the detection of context-specific issues, reducing dependency on manual testing, and promoting more inclusive IoT environments.

Keywords
AI-Driven Accessibility Testing, Smart Home Accessibility, Machine Learning for Accessibility, NLP for Accessibility Evaluation, Computer Vision in Usability Testing.

Reference

[1] Victor Takashi Hayashi et al., “Improving IoT Module Testability with Test-Driven Development and Machine Learning,” 2021 8th International Conference on Future Internet of Things and Cloud (FiCloud), Rome, Italy, pp. 406-412, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Faustinah Nemieboka Tubo, Ikechukwu E. Onyenweet, and Doris C. Asogwa, “Development of an NLP-Driven Computer-Based Test Guide for Visually Impaired Students,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 1-12, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Yi Wu, Jongwoo Lim, and Ming-Hsuan Yang, “Online Object Tracking: A Benchmark,” 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, pp. 2411-2418, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Varun Chandola, Arindam Banerjee, and Vipin Kumar, “Anomaly Detection: A Survey,” ACM Computing Surveys, vol. 41, no. 3, pp. 1- 58, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Alex Graves, Abdel-Rahman Mohamed, and Geoffrey Hinton, “Speech Recognition with Deep Recurrent Neural Networks,” 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada, pp. 6645-6649, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Lorenzo Pellegrini et al., “Latent Replay for Real-Time Continual Learning,” 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, NV, USA, pp. 10203-10209, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Le Lyu, Yang Shen, and Sicheng Zhang, “The Advance of Reinforcement Learning and Deep Reinforcement Learning,” 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms, Changchun, China, pp. 644-648, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Abhishek Dhankar, “Study of Deep Learning and CMU Sphinx in Automatic Speech Recognition,” 2017 International Conference on Advances in Computing, Communications and Informatics, Udupi, India, pp. 2296-2301, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Meghana Pulipalupula et al., “Object Detection Using You Only Look Once (YOLO) Algorithm in Convolution Neural Network (CNN),” 2023 IEEE 8th International Conference for Convergence in Technology (I2CT), Lonavla, India, pp. 1-4, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Kingsley Kuan et al., “Region Average Pooling for Context-Aware Object Detection,” 2017 IEEE International Conference on Image Processing, Beijing, China, pp. 1347-1351, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Reza Hermansyah, and Riyanarto Sarno, “Sentiment Analysis about Product and Service Evaluation of PT Telekomunikasi Indonesia Tbk from Tweets Using TextBlob, Naive Bayes & K-NN Method,” 2020 International Seminar on Application for Technology of Information and Communication (iSemantic), Semarang, Indonesia, pp. 511-516, 2020.
[CrossRef] [Google Scholar] [Publisher Link]