🧠 AI Attention Monitor

A real-time AI-powered system that analyzes user attention using facial landmarks, eye tracking, and head pose estimation.

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🚀 Features

• 🎥 Real-time webcam monitoring
• 🎬 Video upload analysis
• 📊 Live attention graph
• ⚠️ Smart distraction alerts
• 🧠 Focus mode with session tracking
• 👥 Face-wise attention summary (no duplicates)
• 📁 CSV report export

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🧠 How It Works

• Uses MediaPipe Face Mesh for facial landmark detection
• Calculates Eye Aspect Ratio (EAR) for blink detection
• Uses head pose estimation for attention tracking
• Combines signals with temporal smoothing for stability

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🛠 Tech Stack

• Python
• OpenCV
• MediaPipe
• Streamlit
• NumPy / Pandas / Matplotlib

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▶️ How to Run

pip install -r requirements.txt
streamlit run app.py

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📸 Demo

🎥 Live Monitoring

📊 Analytics Dashboard

👥 Face-wise Summary

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📌 Use Cases

• Online learning monitoring
• Productivity tracking
• Classroom analytics
• Focus improvement tools

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🚀 Future Improvements

• Face recognition (identity tracking)
• Mobile app version
• Advanced ML models for attention

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⭐ Acknowledgment

Built as a Deep Learning project with practical real-world application.