Machine Learning Based Assistive Technology for Object Detection and Recognition for Visually Impaired Individuals

Enhancing the independence and daily living activities of visually impaired individuals remains a significant challenge. This research proposes a novel approach that combines cutting-edge computer vision and haptic feedback technologies to address this pressing issue. This approach focuses on small environments, such as a study table, where independent mobility can significantly impact daily tasks. One notable distinction of this system lies in the real-time path planning and obstacle avoid ancecapabilities. This approach dynamically generates obstacle-free paths by continuously analyzing the user's hand movements, allowing users to reach their desired objects effortlessly. This real-time adaptation, combined with intuitive and effective communication, dramatically enhances the user's confidence during navigation. By targeting small environments, the proposed solution provides a tailored solution that optimizes the user's experience in confined spaces. This integration of state-of-the art computervision techniques and haptic feedback technologies offers a unique and holistic approach to independent mobility for visually impaired individuals.