📌 Visual Aided Glass 🕶️ Project Overview
The Visual Aided Glass project is a wearable assistive device designed to help visually impaired individuals navigate their environment safely and independently. The system integrates IoT sensors, camera vision, and audio feedback to detect obstacles, recognize objects, and provide real-time assistance.
🚀 Features
Obstacle detection using ultrasonic/IR sensors.
Object recognition with a camera and ML model.
Voice/audio feedback through an earpiece.
GPS integration for navigation and emergency assistance.
Lightweight and wearable design for daily use.
🛠️ Tech Stack
Hardware: Arduino/Raspberry Pi, Ultrasonic Sensors, Camera Module, Bluetooth Module, Speakers/Earphones.
Software: Python, OpenCV, TensorFlow/PyTorch, Arduino IDE.
APIs: Text-to-Speech, GPS Navigation APIs.
⚡ Installation & Usage
Install all required Python libraries from requirements.txt.
Upload microcontroller code via Arduino IDE or Raspberry Pi.
Run the object detection model using:
python main.py
Connect the device, wear the glass, and test obstacle/object detection.
📌 Future Enhancements
AI-based facial recognition for known contacts.
Multilingual audio feedback.
Integration with cloud services for real-time assistance.
📌 README for ML-Based Disease Outbreak Prediction System 🧬 Project Overview
The Disease Outbreak Prediction System leverages machine learning to analyze epidemiological, environmental, and social data to predict potential disease outbreaks. The goal is to assist public health authorities in early detection, risk assessment, and prevention strategies.
🚀 Features
Data preprocessing from epidemiological and environmental datasets.
Time-series forecasting of disease spread.
Visualization of outbreak hotspots via interactive dashboards.
Machine learning models for prediction (SVM, Random Forest, LSTM).
Real-time alerts for potential outbreak risks.
🛠️ Tech Stack
Programming: Python
Libraries: Pandas, NumPy, Scikit-learn, TensorFlow/Keras, Matplotlib,
Visualization: Dash, Streamlit, or Tableau
📌 Future Enhancements
Integration with real-time healthcare APIs.
Mobile app for health workers and citizens.
Use of graph neural networks for modeling disease spread.
- ESP32 microcontroller
- Ultrasonic sensor (HC-SR04)
- Buzzer for audio alerts
- Emergency push button
- Rechargeable battery
- Smart glasses frame
- Obstacle Detection – Ultrasonic sensor emits sound waves and measures reflection time to detect objects.
- Processing – ESP32 processes the sensor data in real-time.
- Alert System – Audio feedback (buzzer) warns the user as they approach an obstacle.
- Emergency Feature – Pressing the emergency button triggers an alarm or sends a help notification.