International Journal of Science, Engineering and TechnologyAuthors: Tavhare Sakshi Hemant, Bangar Abhishek Popat, Bhosale Sachin Bajirao
Abstract: Visually impaired individuals face daily mobility and information-access challenges that traditional aids, such as white canes or guide dogs, cannot fully resolve, particularly regarding dynamic obstacles, environmental hazard detection, and reading printed text. This paper presents a structured overview of the VisionMate Multimode AI Smart Glasses ecosystem, a wearable assistive technology designed to bridge these gaps through real-time computer vision, optical character recognition, GPS mapping, and audio feedback. The system architecture distributes workloads between a lightweight, camera-enabled smart glasses frame and a companion mobile processing unit running offline-capable deep learning models. Key functional modes include real-time obstacle detection with alert throttling, instant text-to-speech conversion using OCR pipelines, spatial GPS navigation, and emergency SOS integration. However, assistive wearable deployments face ongoing design constraints, including battery limitations, processing latency, and the need for seamless, hands-free accessibility controls. The system demonstrates how multimodal AI integration can enhance autonomy, spatial awareness, and independence for users with visual impairments.
