The Edge-based Smart Assistance System is an innovative AI-powered project developed to support the independent mobility and safety of visually impaired individuals. Leveraging the power of Computer Vision, Edge Computing, and Voice AI, the system acts as a real-time intelligent assistant that helps users navigate their surroundings safely and confidently.
This solution was developed using YOLOv8 object detection (via Roboflow) on a compact yet powerful Khadas VIM1S edge device—providing ultra-low latency performance without the need for cloud connectivity. From obstacle detection to real-time feedback, the system is designed to empower users with situational awareness in an efficient, intuitive, and wearable form.
The goal was to build a portable, smart, and energy-efficient device that could serve as a digital companion for visually impaired people. Through AI-driven guidance and multisensory feedback, the device enables users to make informed decisions while walking in unfamiliar environments.
YOLOv8 for real-time object detection
Roboflow for dataset training and deployment
Khadas VIM1S (Edge Device) for local inference
AI Voice Interface for directional and object guidance
Ultrasonic sensors & Vibratory Motor for haptic feedback
Weather API Integration for real-time environmental updates
This platform was developed to enhance independence, confidence, and safety for individuals with visual impairments. By combining smart technologies with practical design, the system not only provides real-time assistance but also promotes digital inclusivity and accessibility.
“It’s not just a tool—it’s a digital companion that walks with you, hears for you, and sees for you.”
Visually impaired individuals.
Elderly people with navigation difficulties.
Healthcare organizations and NGOs focused on accessibility.
Object Detection: YOLOv8 + Roboflow
Hardware: Khadas VIM1S, Ultrasonic Sensor, Vibration Motor
Programming: Python, OpenCV, TensorFlow Lite
Voice AI: Custom voice command module
Weather: OpenWeather API Integration
Research & Dataset Preparation: 1 Week.
Model Training & Optimization: 2 Weeks.
Hardware Integration & Testing: 1 Week.
Final Deployment & User Trials: 1 Week
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