👁️🫀 Heart Rate Prediction Using Retina & Pupil Analysis

AI-Powered Real-Time Heart Rate Estimation Using Computer Vision, Signal Processing, and Machine Learning

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📌 Overview

Heart Rate Prediction Using Retina & Pupil Analysis is an intelligent health-monitoring system that estimates a user's heart rate (BPM) from live webcam video by analyzing subtle variations in the eye region.

The project combines:

• Computer Vision
• MediaPipe Face Mesh
• Eye Tracking
• Signal Processing
• FFT Analysis
• Machine Learning
• Real-Time Dashboard Analytics

to create a complete end-to-end physiological monitoring application.

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🚀 Key Features

👤 Real-Time Face Detection

• Live webcam monitoring
• Face tracking
• Face presence detection

👁️ Dual Eye Tracking

• Left eye landmark detection
• Right eye landmark detection
• Eye ROI analysis

📈 Signal Processing Pipeline

• Intensity signal extraction
• Noise reduction
• Bandpass filtering
• FFT transformation
• Frequency analysis

❤️ Heart Rate Estimation

Real-time BPM prediction using:

Heart Rate (BPM) = Frequency × 60

🤖 Machine Learning Integration

Models Used:

• Linear Regression
• Random Forest Regressor
• Gradient Boosting Regressor
• Support Vector Regressor
• Ensemble Learning

📊 Interactive Dashboard

• Live Face Feed
• Eye Analysis Feed
• Heart Rate Monitoring
• Frequency Monitoring
• Signal Quality Tracking
• Real-Time Signal Graph
• Real-Time FFT Spectrum

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🏗️ System Architecture

Webcam
   ↓
Face Detection
   ↓
Eye Tracking
   ↓
ROI Extraction
   ↓
Feature Extraction
   ↓
Signal Processing
   ↓
FFT Analysis
   ↓
Heart Rate Estimation
   ↓
Interactive Dashboard

📷 Project Screenshots Dashboard

Face Detection

Eye Analysis

Live Signal Monitoring

🛠️ Technology Stack Category Technologies Backend Flask Computer Vision OpenCV, MediaPipe Machine Learning Scikit-Learn Signal Processing SciPy, NumPy Data Handling Pandas Model Storage Joblib Frontend HTML, CSS, JavaScript Visualization Chart.js 📂 Project Structure Heart_Rate_Prediction_Using_Pupil/

(Include your folder tree here)

⚙️ Installation Clone Repository git clone https://github.com/yourusername/Heart_Rate_Prediction_Using_Pupil.git Navigate cd Heart_Rate_Prediction_Using_Pupil Install Dependencies pip install -r requirements.txt Run Application python app.py 🎯 Future Enhancements Remote PPG Signal Extraction Deep Learning-Based Heart Rate Prediction Health Risk Detection Multi-Person Monitoring Cloud Dashboard Mobile Deployment 📊 Learning Outcomes

This project demonstrates:

Computer Vision Eye Tracking Physiological Signal Analysis Frequency Domain Processing Machine Learning Real-Time Dashboard Development Full Stack AI Application Development 👨‍💻 Author Sai Srikar

AI • Machine Learning • Computer Vision • Full Stack Development

⭐ Support

If you found this project useful:

⭐ Star the repository

🍴 Fork the repository

📢 Share with others

"Transforming Eye Signals into Physiological Intelligence."

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8. Push to GitHub

git init

git add .

git commit -m "Initial Commit - Heart Rate Prediction Using Retina and Pupil Analysis"

git branch -M main

git remote add origin YOUR_REPO_URL

git push -u origin main