Inclusive AI & Edge IoT Engineering: Multimodal Assistive System for Educational & Spatial Accessibility in STEM

This project encompasses the full lifecycle conceptualization, hardware-software integration, and deployment plan for EduNav+, an AI-powered educational and indoor navigation assistant designed to enhance classroom participation and mobility for blind and visually impaired students in STEM disciplines. Spearheaded at Jomo Kenyatta University of Agriculture and Technology (JKUAT), the project combines embedded IoT hardware, computer vision models, mathematical OCR, and tactile/speech feedback systems into an integrated accessibility platform. The system architecture features a wearable unit—built on a Raspberry Pi 4, VL53L0X Time-of-Flight sensors, USB webcams, and HC-05 Bluetooth modules—paired with an Android companion application. The software pipeline leverages open-source machine learning frameworks, including YOLO for real-time obstacle detection and spatial navigation, Mathpix for parsing complex mathematical equations, and Whisper for speech recognition and lecture transcription. Whiteboard content and live instructional streams are processed via OCR and delivered to users through synthesized text-to-speech or a refreshable Braille pad. Guided by a seven-phase development workplan and a cost-conscious budget of Ksh 55,500 (~£300), the initiative incorporates empirical user testing with visually impaired students, establishing a scalable, educationally aligned assistive ecosystem that bridges the digital divide in scientific higher education.