A Tensor Flow Lite-Powered Wearable Navigation Assistant Using Raspberry Pi for Real-Time Obstacle Detection and Autonomous Mobility in the Visually Impaired

In year 2023, around 2.2 billion people globally have near or distance visual impairment. Previous systems lacked comprehensive functionality, focusing only on basic obstacle detection or standalone features like fall detection. Moreover, the studies struggled with bulky designs, poor low-light performance, and limited environmental awareness. To address these challenges, the study develops ObstaSense, a wearable Electronic-Travel-Aid (ETA) for obstacle detection and navigation assistance. The system employed TensorFlow Lite, a Raspberry Pi-5, and a Pi-Camera Module-V3 to detect objects (e.g., people, potholes, vehicles) and relay avoidance instructions via Bluetooth earbuds. Its Real-Time Navigation (RTN) feature combined Global Positioning System (GPS), a compass sensor, and Plus Codes for precise guidance, enhanced by Google’s Speech-To-Text (STT) and Text-to-Speech (TTS). Operating at 4–10 Frames Per Second (FPS), ObstaSense further integrated the Gemini Application programming interface (API) for multilingual (50-languages) image-to-text conversion. The system achieved consistent results by leveraging precise RTN functionality, which uses compass sensor data and vibration feedback to guide users accurately. Offline dataset training and evaluation were conducted solely to support the deployment of a real-time embedded assistive system on Raspberry Pi 5. Obstacle avoidance performance varied across rows, with the highest accuracy (100%) in the first row, followed by 66.7% in the third row and 50% in the second row. ObstaSense aids visually impaired, elderly, and cognitively impaired users, aligning with Sustainable Development Goals (SDGs) 3 and 10 for inclusive well-being.