Project Description:

The IoT Health Gateway is a centralized health monitoring system that connects multiple wearable and medical IoT devices to collect, analyze, and transmit real-time health data to patients, doctors, or caregivers. It acts as a communication bridge between health monitoring devices and cloud or web applications for remote health tracking, diagnosis, and alerts.

This project is ideal for hospitals, remote clinics, elderly care, and home-based patient monitoring.

Technologies Used:

• Backend: Node.js / PHP / Java

• Frontend: HTML, CSS, Bootstrap, JavaScript

• IoT Devices: ESP32 / Raspberry Pi with sensors (heart rate, SpO₂, BP, temperature)

• Communication: MQTT / HTTP / Bluetooth / Wi-Fi

• Database: MySQL / Firebase

• Cloud Integration: ThingsBoard / AWS IoT Core / Google Firebase (optional)

Key Features:

1. Multi-Sensor Integration:

   • Connects to sensors like:

     • Heart rate monitor

     • Body temperature sensor

     • Blood pressure monitor

     • Blood oxygen (SpO₂) sensor

     • Glucose monitor (optional)

     • ECG sensor (optional)

2. Real-Time Data Upload:

   • Sensor data is pushed to a backend server/cloud in real-time.

   • Data is timestamped and stored securely for medical analysis.

3. User Dashboard:

   • Patients and doctors can log in to view live vitals, historical trends, and alerts.

4. Health Alerts & Notifications:

   • Sends alerts (SMS/email/WhatsApp) if vitals go out of safe range (e.g., heart rate too high).

   • Can alert emergency contacts or doctors.

5. Data Visualization:

   • Graphs for vital sign trends (daily/weekly/monthly)

   • Downloadable health reports in PDF

6. Doctor-Patient Communication:

   • Secure chat or recommendation section (optional)

   • Upload prescriptions and treatment plans

7. Remote Access:

   • Accessible from desktop or mobile browser, or via companion app (optional)

System Workflow:

1. IoT sensors collect health data from the patient.

2. Microcontroller (ESP32 or Pi) processes the data and sends it to the cloud or backend.

3. Backend stores the data and triggers alerts if thresholds are crossed.

4. Doctors and patients can access the health data via web dashboard.

5. Optional machine learning module detects abnormal health patterns.