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 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.