Design and Implementation of Real Time Patient Monitoring System

Abstract

The rapid advancement of Internet of Things (IoT) technology has significantly impacted the healthcare industry, enabling smarter and more efficient remote monitoring systems. This project presents the design and implementation of an IoT-based health monitoring system capable of measuring body temperature, heart rate, and blood oxygen saturation (SpO?) levels using embedded systems. The core of the system is built around the Raspberry Pi Pico microcontroller, which interfaces with two biomedical sensors: the LM35 analog temperature sensor and the MAX30100 pulse oximeter and heart rate sensor. The LM35 sensor provides precise body temperature readings by converting analog voltage into temperature values in Celsius. The MAX30100 sensor, on the other hand, combines two LEDs (infrared and red) and a photodetector to measure heart rate and SpO? levels based on pulse oximetry principles. The sensor data collected by the Pico is processed and transmitted over UART (Universal Asynchronous Receiver-Transmitter) to a NodeMCU ESP8266 Wi-Fi module. Communication between the Pico and NodeMCU is established using SoftwareSerial, where the NodeMCU acts as a bridge between the microcontroller and the internet. Once the NodeMCU receives the serial data, it parses the values and uploads them in real-time to the Blynk IoT platform. Blynk provides a graphical user interface via a smartphone app where users can monitor temperature, heart rate, and SpO? on customizable virtual pins (e.g., V0, V1, V2). This remote access capability allows doctors, family members, or caregivers to monitor the health of individuals from anywhere at any time, thereby improving patient care and response times in emergency situations. The system is powered via USB or battery and is compact, making it suitable for home-based patient monitoring, telemedicine applications, fitness tracking, and rural health environments where medical facilities may be limited. Overall, this project demonstrates a low-cost, energy-efficient, and scalable solution for remote health tracking by combining embedded hardware, biomedical sensors, and IoT connectivity, all without the need for expensive hospital-grade monitoring systems.

Introduction

The project presents a cost-effective, portable, and scalable IoT-based health monitoring system for real-time tracking of vital signs such as body temperature, heart rate, and SpO? (oxygen saturation). It addresses the limitations of traditional periodic healthcare by offering continuous and remote monitoring through embedded systems and cloud connectivity.

Key Components:

• Sensors:

  • LM35: Analog temperature sensor (accurate, low-cost).

  • MAX30100: Measures heart rate and SpO? using red/infrared LEDs.

• Microcontrollers:

  • Raspberry Pi Pico: Acquires and processes sensor data.

  • NodeMCU ESP8266: Transmits data to the cloud via Wi-Fi.

• Cloud Platform:

  • Blynk IoT: Displays real-time health data on a mobile app with virtual pins for each parameter (Temp: V0, HR: V1, SpO?: V2).

Objectives:

1. Design a compact and portable health device.

2. Enable real-time data acquisition using embedded systems.

3. Support wireless communication for remote access.

4. Provide a user-friendly mobile interface via Blynk.

5. Make the system affordable and accessible in low-resource settings.

6. Enable emergency alerts for abnormal readings.

7. Ensure scalability for future sensor integration and analytics.

System Architecture:

• Sensor Data Acquisition:

  • LM35 outputs analog voltage → converted to temperature.

  • MAX30100 uses I²C to send raw data → processed for HR and SpO?.

• Data Processing:

  • Raspberry Pi Pico formats data string and sends via UART.

• Wireless Data Transmission:

  • NodeMCU receives serial data and updates Blynk in real time.

• Mobile Interface:

  • Blynk app displays health metrics and can trigger alerts.

Performance & Results:

• Accuracy:

  • Temperature error margin: ±0.2°C.

  • SpO? accuracy: ±2%.

  • Reliable heart rate detection in the 50–120 bpm range.

• Latency:

  • Real-time updates with ~2 seconds delay.

• Stability:

  • Withstood Wi-Fi disruptions and light movement.

• Power Efficiency:

  • 12 hours operation on a standard power bank.

• Limitations:

  • No onboard data logging.

  • Sensitive to light interference.

  • No built-in data encryption.

Applications:

• Home-based monitoring

• Elderly care

• Rural telemedicine

• Fitness tracking

• Post-operative or chronic patient care

Conclusion

The IoT-based health monitoring system developed in this project successfully demonstrates a practical and low-cost solution for real-time tracking of vital health parameters such as body temperature, heart rate, and SpO? levels. By integrating the Raspberry Pi Pico microcontroller with the LM35 temperature sensor and MAX30100 pulse oximeter, and leveraging the NodeMCU ESP8266 for wireless data transmission, the system provides accurate and continuous health data to the Blynk IoT platform. The mobile app interface offers remote visibility, allowing caregivers and medical professionals to monitor patients from anywhere at any time. The system proved to be energy-efficient, responsive, and user-friendly, making it especially suitable for home-based patient monitoring, elderly care, fitness tracking, and healthcare in rural areas with limited infrastructure. Despite minor limitations such as the absence of offline data storage and encryption, the project fulfills its core objectives and serves as a strong prototype for future development. The current system lays a robust foundation for more advanced health monitoring applications and can be enhanced in several directions. Future improvements may include the integration of additional biomedical sensors such as ECG, blood pressure, body movement (accelerometer), or fall detection modules to expand its functionality. Data logging capabilities and cloud database storage can be introduced for historical data analysis and long-term health tracking. Machine learning models could be integrated to provide predictive health analytics or detect anomalies in vital signs automatically. Security features like data encryption and secure cloud communication protocols should be implemented to ensure patient data privacy and compliance with medical standards. The system can also be expanded for multi-patient monitoring in hospitals or remote clinics, and integrated with other healthcare platforms for telemedicine applications. With further development, this project has the potential to evolve into a scalable, intelligent, and medically relevant IoT solution for widespread use in modern healthcare systems.

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Copyright

Copyright © 2025 Mrs. K. Bhavani, B. Shivani, M. Bharath Pal Reddy, K. Srikanth. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.