🩺 Health Monitoring System

A real-time IoT health monitoring system that collects vital signs (heart rate, SpO₂, temperature) from wearable sensors, and logs all data to a Flask-based web dashboard for visualization and analytics.

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⚙️ Hardware Components

Component	Function
ESP8266	WiFi microcontroller (data transmission)
MAX30100	Heart rate and SpO₂ sensor
DS18B20	Digital temperature sensor
Buzzer	Audio alert in case of abnormal values or fall
Power	3.3V regulated supply

📄 Refer to hardware setup guide for circuit connection and Arduino IDE configuration.

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📡 How It Works

1. Sensor Data Collection
   ESP8266 reads from:

   • MAX30100 → Heart_rate, SpO₂
   • DS18B20 → Temperature

2. Alert System

   • If any vital drops below threshold or is detected → buzzer rings.
   • Simultaneously, Telegram bot sends alert to caretaker.

3. Data Transmission

   • ESP8266 sends data to Flask server via HTTP POST (/add_log) endpoint.

4. Web Dashboard

   • Web UI shows real-time logs and plots (heart rate, SpO₂, temperature).
   • Alerts are also logged and visualized for analytics.

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🌐 Software Architecture

Backend: Flask + MongoDB
Frontend: HTML + Chart via Matplotlib
Data Storage: MongoDB (Local/Cloud)
IoT Firmware: Arduino (C++)

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💾 Setting Up the Server & Backend

🔁 Clone the Repository

git clone https://github.com/LegitCoconut/health-monitor.git
cd health-monitor

🧪 Python Environment

Install virtual environment (optional but recommended):

python3 -m venv myenv

Activate it:

• Linux/macOS:

  source myenv/bin/activate

• Windows:

  myenv\Scripts\activate

📦 Install Dependencies

pip install -r requirements.txt

⚙️ MongoDB Setup

Local MongoDB:

client = MongoClient("mongodb://localhost:27017/")

Cloud MongoDB (e.g., Atlas):

Set this as an environment variable:

export MONGO_URI="your_mongo_connection_string"

On Windows (CMD):

set MONGO_URI="your_mongo_connection_string"

You can also create a .env file and load it manually.

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🚀 Run the Flask Server

python app.py

Flask app will run at: http://localhost:5000

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🧪 Generate Test Data

To simulate sensor data:

python test.py

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☁️ Deployment on Vercel (Optional)

1. Fork or push the project to your GitHub account.
2. Login to Vercel, import the GitHub repo.
3. In Project Settings > Environment Variables, add:

Key	Value
MONGO_URI	your MongoDB connection URI

4. Set Framework Preset as Other or Flask.
5. Vercel will auto-deploy your project.

⚠️ Note: Vercel is for frontend apps. For backend Flask, use Render, Railway, or deploy on VPS (DigitalOcean, EC2, etc.).

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🧠 Code Overview (Crucial Sections)

app.py (Flask Backend)

@app.route("/add_log", methods=["POST"])

Receives JSON data from ESP8266:

{
  "Heart_beat_rate": 80,
  "Sp02_level": 96,
  "Temperature": 36.5,
  "time_of_check": "2025-04-04T12:30:00"
}

Inserts into MongoDB:

logs_collection.insert_one(data)

@app.route("/stats")

Calculates average, peak, and low values for each vital using:

def compute_stats(field):
    ...

@app.route("/generate_graph")

Generates time-series graph using matplotlib, returns image as PNG:

plt.savefig(img_io, format='png')
return send_file(img_io, mimetype='image/png')

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📊 Web Dashboard Routes

Endpoint	Description
/	Main dashboard (index.html)
/get_logs	Returns all logs (JSON)
/add_log	Accepts data from device (POST)
/stats	Returns statistical metrics
/generate_graph	Returns graph (PNG image)

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📦 Directory Structure

health-monitor/
│
├── app.py             # Main Flask backend
├── test.py            # Dummy data injector
├── requirements.txt   # Python dependencies
├── templates/
│   └── index.html     # Web dashboard UI
└── static/            # (Optional CSS/JS)

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🛡️ Security Recommendations

• Validate incoming sensor data types.
• Use .env for secrets like DB credentials.
• Add basic auth to dashboard (Flask-login or JWT).
• Secure MongoDB with IP whitelisting or VPN.

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