Enhancing Accessibility Through Machine Learning: A Review on Visual and Hearing Impairment Technologies
Assistive technologies powered by machine learning are transforming the way sensory impairments are addressed, offering innovative solutions for individuals with hearing and visual disabilities. This paper provides a comprehensive review of machine learning algorithms designed to enhance accessibili...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10872982/ |
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| author | Pal Patel Shreyansh Pampaniya Ananya Ghosh Ritu Raj Deepa K Saravanakumar Kandasamy |
| author_facet | Pal Patel Shreyansh Pampaniya Ananya Ghosh Ritu Raj Deepa K Saravanakumar Kandasamy |
| author_sort | Pal Patel |
| collection | DOAJ |
| description | Assistive technologies powered by machine learning are transforming the way sensory impairments are addressed, offering innovative solutions for individuals with hearing and visual disabilities. This paper provides a comprehensive review of machine learning algorithms designed to enhance accessibility for these groups. For hearing impairments, the analysis focuses on advanced models such as Support Vector Machines (SVM), Random Forests (RF), and Multi-Layer Perceptrons (MLP), examining their effectiveness in auditory assistive applications. In the context of visual impairments, state-of-the-art object detection frameworks like You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), and RetinaNet are evaluated for their capability to enable real-time object recognition and navigation aids. The study also reviews the Generative Artificial Intelligence based applications for visual and hearing impaired use cases. The study addresses the unique challenges and requirements associated with each type of sensory impairment, with particular emphasis on the customization and fine-tuning of machine learning models for personalized, effective solutions. Additionally, it highlights the transformative potential of deep learning models in advancing assistive technologies, ultimately aiming to enhance the quality of life for individuals with sensory disabilities. By advocating for the development and integration of such technologies, this paper underscores the importance of inclusivity and empowerment in creating a more equitable society. |
| format | Article |
| id | doaj-art-9633a477b22b4dd2ac833382c3a968ec |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9633a477b22b4dd2ac833382c3a968ec2025-08-20T02:45:32ZengIEEEIEEE Access2169-35362025-01-0113332863330710.1109/ACCESS.2025.353908110872982Enhancing Accessibility Through Machine Learning: A Review on Visual and Hearing Impairment TechnologiesPal Patel0https://orcid.org/0009-0004-0929-9181Shreyansh Pampaniya1https://orcid.org/0009-0002-4492-243XAnanya Ghosh2https://orcid.org/0009-0005-7784-007XRitu Raj3https://orcid.org/0009-0003-6575-1323Deepa K4https://orcid.org/0000-0001-5294-5522Saravanakumar Kandasamy5https://orcid.org/0000-0002-9083-7271School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, IndiaAssistive technologies powered by machine learning are transforming the way sensory impairments are addressed, offering innovative solutions for individuals with hearing and visual disabilities. This paper provides a comprehensive review of machine learning algorithms designed to enhance accessibility for these groups. For hearing impairments, the analysis focuses on advanced models such as Support Vector Machines (SVM), Random Forests (RF), and Multi-Layer Perceptrons (MLP), examining their effectiveness in auditory assistive applications. In the context of visual impairments, state-of-the-art object detection frameworks like You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), and RetinaNet are evaluated for their capability to enable real-time object recognition and navigation aids. The study also reviews the Generative Artificial Intelligence based applications for visual and hearing impaired use cases. The study addresses the unique challenges and requirements associated with each type of sensory impairment, with particular emphasis on the customization and fine-tuning of machine learning models for personalized, effective solutions. Additionally, it highlights the transformative potential of deep learning models in advancing assistive technologies, ultimately aiming to enhance the quality of life for individuals with sensory disabilities. By advocating for the development and integration of such technologies, this paper underscores the importance of inclusivity and empowerment in creating a more equitable society.https://ieeexplore.ieee.org/document/10872982/Visual impairmenthearing impairmentmachine learningdeep learning |
| spellingShingle | Pal Patel Shreyansh Pampaniya Ananya Ghosh Ritu Raj Deepa K Saravanakumar Kandasamy Enhancing Accessibility Through Machine Learning: A Review on Visual and Hearing Impairment Technologies IEEE Access Visual impairment hearing impairment machine learning deep learning |
| title | Enhancing Accessibility Through Machine Learning: A Review on Visual and Hearing Impairment Technologies |
| title_full | Enhancing Accessibility Through Machine Learning: A Review on Visual and Hearing Impairment Technologies |
| title_fullStr | Enhancing Accessibility Through Machine Learning: A Review on Visual and Hearing Impairment Technologies |
| title_full_unstemmed | Enhancing Accessibility Through Machine Learning: A Review on Visual and Hearing Impairment Technologies |
| title_short | Enhancing Accessibility Through Machine Learning: A Review on Visual and Hearing Impairment Technologies |
| title_sort | enhancing accessibility through machine learning a review on visual and hearing impairment technologies |
| topic | Visual impairment hearing impairment machine learning deep learning |
| url | https://ieeexplore.ieee.org/document/10872982/ |
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