IoT Based Connected Care Health Monitoring System

Techs: ESP32, Pulse Sensor, MAX30102, DHT11, OLED, MQTT, HiveMQ Cloud, Arduino IDE, Visual Studio code.
Department: Electrical Engineering
MS Team URL: URL not found

Real-time IoT health monitoring system for monitoring patient vitals remotely using ESP32 sensors and MQTT cloud communication.

Objectives

The objective of this project is to develop a smart IoT-based health monitoring system capable of measuring and displaying patient health parameters in real time. The system uses ESP32 with health sensors to collect body temperature, heart rate, and oxygen level data. The collected data is transmitted through MQTT protocol to a cloud dashboard for remote monitoring. The project aims to provide low-cost, portable, and efficient healthcare monitoring for hospitals and home patients.

Socio-Economic Benefit

This project can improve healthcare services by enabling remote patient monitoring and reducing unnecessary hospital visits. It is useful for elderly and critical patients who require continuous observation. The system is cost-effective and suitable for developing areas where advanced healthcare equipment is expensive. It also supports smart healthcare initiatives and digital health transformation using IoT technology.

Methodologies

The project follows an IoT-based embedded system methodology. Health sensors are interfaced with ESP32 microcontroller for data acquisition. Sensor readings are processed and transmitted securely using MQTT protocol through HiveMQ Cloud. A web-based dashboard is developed using HTML, CSS, JavaScript, and Chart.js in Visual Studio Code for real-time visualization of patient data. Testing and calibration are performed to ensure accurate readings and stable communication.

Outcome

The developed system successfully monitors health parameters such as heart rate, SpO2, and temperature in real time. Data is displayed on both OLED and online dashboard through MQTT cloud communication. The system demonstrates reliable wireless monitoring with low power consumption and user-friendly interface. The project can be extended further with AI-based health prediction and emergency alert systems.

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