Swasthya - Air Quality & Health Correlation Analysis

A full-stack MERN application that analyzes the relationship between live air pollution levels and disease spread using IoT sensors and federated learning.

🚀 Features

• Modern Glassmorphism UI - Beautiful 3D glassmorphism design with smooth animations
• Real-time Data Visualization - Interactive charts showing AQI, pollutants, and health metrics
• MongoDB Backend - Scalable data storage with Express.js REST API
• IoT Integration - Support for real-time sensor data ingestion
• Responsive Design - Works seamlessly on desktop and mobile devices

📁 Project Structure

Swasthya/
├── backend/                 # Express.js server
│   ├── src/
│   │   ├── server.js       # Main server file
│   │   ├── models/         # MongoDB models
│   │   └── routes/         # API routes
│   ├── scripts/
│   │   └── seedDatabase.js # Database seeding script
│   └── .env                # Environment variables
├── frontend/               # React application
│   ├── src/
│   │   ├── pages/         # Page components
│   │   ├── App.jsx        # Main app component
│   │   └── main.jsx       # Entry point
│   └── package.json
└── iot/                    # IoT sensor data
    └── sensorData.json     # Sample sensor data

🛠️ Tech Stack

Frontend

• React 19
• React Router DOM
• Recharts (Data Visualization)
• Vite (Build Tool)
• Pure CSS (Glassmorphism Styling)

Backend

• Node.js
• Express.js
• MongoDB Atlas
• Mongoose ODM
• CORS

📦 Installation

Prerequisites

• Node.js (v20.19+ or v22.12+)
• MongoDB Atlas Account (or local MongoDB)

Backend Setup

1. Navigate to backend directory:

cd backend

2. Install dependencies:

npm install

3. Create a .env file in the backend directory:

cp .env.example .env

4. Update the .env file with your MongoDB connection string:

MONGODB_URI=your_mongodb_connection_string_here
PORT=5000

5. Seed the database with sample IoT data:

npm run seed

5. Start the backend server:

npm start

The server will run on http://localhost:5000

Frontend Setup

1. Navigate to frontend directory:

cd Frontend

2. Dependencies are already installed

3. Start the development server:

npm run dev

The frontend will run on http://localhost:5173

🎯 Usage

Accessing the Application

1. Homepage: Navigate to http://localhost:5173

   • Modern glassmorphism landing page
   • Features overview
   • Navigation to dashboard

2. Dashboard: Click "View Dashboard" or go to http://localhost:5173/dashboard

   • Real-time AQI and pollution metrics
   • Interactive charts and graphs
   • Health impact analysis
   • Location-based data

API Endpoints

• GET /api/sensor-data - Get all sensor data (limited to 50 records)
• GET /api/sensor-data/latest - Get latest 10 sensor readings
• GET /api/sensor-data/location/:area - Get data by location area
• GET /api/sensor-data/stats - Get aggregated statistics
• POST /api/sensor-data - Add new sensor data

Testing the API

# Get latest sensor data
curl http://localhost:5000/api/sensor-data/latest

# Get statistics
curl http://localhost:5000/api/sensor-data/stats

📊 Data Metrics

The system monitors:

Air Quality

• AQI (Air Quality Index)
• PM10 & PM2.5 (Particulate Matter)
• NO₂ (Nitrogen Dioxide)
• SO₂ (Sulfur Dioxide)
• O₃ (Ozone)

Weather

• Temperature
• Humidity
• Wind Speed

Health Impact

• Respiratory Cases
• Cardiovascular Cases
• Hospital Admissions
• Health Impact Score
• Health Impact Classification

🎨 Design Features

• 3D Glassmorphism: Modern glass-like UI components with backdrop blur
• Animated Background: Floating gradient spheres with smooth animations
• Responsive Layout: Mobile-first design that works on all devices
• Live Data Indicators: Real-time pulse animations
• Interactive Charts: Smooth, animated Recharts visualizations
• Color-coded AQI: Easy-to-understand health risk levels

🔄 Adding New Sensor Data

1. Add data to iot/sensorData.json following the existing format
2. Run the seed script: cd backend && npm run seed
3. Refresh the dashboard to see new data

🚧 Future Enhancements

• Federated Learning Integration
• WebSocket for real-time updates
• User Authentication (Government/Hospital portals)
• AWS Deployment
• ML Model Integration
• Mobile App
• Email Alerts for High AQI

📝 Environment Variables

Create a .env file in the backend directory:

MONGODB_URI=your_mongodb_connection_string_here
PORT=5000

Replace your_mongodb_connection_string_here with your actual MongoDB Atlas connection string.

🤝 Contributing

This is a full-stack project for air quality and health correlation analysis. Contributions are welcome!

📄 License

MIT License

👨‍💻 Developer

Built with ❤️ for better public health decisions

---

Note: This is an MVP (Minimum Viable Product). The federated learning, IoT sensor integration, and AWS deployment will be implemented in future phases.