Enhancing Accessibility for Visually Impaired Individuals

Abstract

Abstract This research addresses the challenge of enhancing dining autonomy for visually impaired individuals by designing, developing, and evaluating a mobile application for real-time food recognition. The primary research problem is the lack of accessible and reliable food identification solutions tailored to visually impaired users. To address this, the study explores the integration of advanced object detection and intuitive interaction technologies in a mobile context. The proposed solution employs YOLOv8, a lightweight deep learning model optimized for mobile deployment, paired with a speech-based interface for voice commands and touch gestures. The application enables users to capture a video of their meal and provides spoken descriptions of detected food items through Text-to-Speech (TTS) technology. The backend system leverages a TensorFlow Lite implementation, ensuring low-latency performance on mid-range Android devices. Key system specifications include a Qualcomm Snapdragon 720G processor, 4GB RAM, and Android 11, with backend services running on Firebase for data storage and model updates. Evaluation methods included performance testing and user accessibility studies with visually impaired participants. Results demonstrate high detection accuracy, and positive user feedback. These findings validate the technical reliability and user satisfaction of the solution. This study contributes to the field by demonstrating the feasibility of accessible food recognition technologies through innovative system design and user-centered evaluation. The proposed framework can serve as a foundation for broader applications in accessible technology, improving the quality of life for visually impaired individuals.