Smart Monitoring System for Patient

Abstract

With the increasing need for real-time and remote healthcare solutions, this project introduces the creation of a Smart Patient Monitoring System based on Internet of Things (IoT) technologies. The system is intended to monitor continuously vital health parameters including heart rate, body temperature, blood oxygen saturation (SpO?), and motion. Utilizing sensors such as MAX30100, AD8232, BMP180, and DHT22, processed physiological data through an ESP32 microcontroller, and wirelessly transmitted via GPRS and Bluetooth modules. Visually and auditory alerts through immediate OLED display and buzzer warning for abnormal levels, and remotely accessed through cell phones for direct monitoring by health care professionals at real-time enable monitoring of the patient\'s well-being. This system strives to reduce the need for manual intervention, shorten diagnostic delays, and improve patient care through prompt alerts and real-time monitoring. Its low-cost, scalable nature makes it perfect for deployment in both hospital settings and home care environments, particularly in areas with sparse medical infrastructure. The research emphasizes the pragmatic integration of embedded systems and IoT to create a robust, responsive, and user-friendly health monitoring platform.

Introduction

The integration of technology, especially the Internet of Things (IoT), in healthcare has revolutionized patient monitoring by enabling continuous, real-time tracking of vital signs remotely, reducing reliance on intermittent manual checks. This is particularly important for chronic patients, bedridden individuals, and those in remote areas.

The paper presents the design and implementation of a cost-effective Smart Patient Monitoring System using biomedical sensors connected to an ESP32 microcontroller. It monitors key health parameters such as heart rate, blood oxygen (SpO?), ECG, and temperature, displaying data locally on an OLED screen and sending alerts via Bluetooth and GSM modules. An audible buzzer alerts caregivers to emergencies. The system is scalable and suited for hospitals, elder care, or personal use.

A literature survey highlights advances in IoT healthcare applications, including challenges like data security, device compatibility, and the need for standardized protocols. Studies also emphasize the potential of machine learning and AI to enhance predictive health monitoring.

Methodology details sensor choices: MAX30100 for SpO? and heart rate, AD8232 for ECG, and DHT11 for temperature, interfaced with the ESP32. The system processes data in real time, activates alarms for abnormal readings, and supports wireless data transmission.

Testing on healthy subjects showed reliable monitoring of vital signs and demonstrated the system’s capacity for early detection of abnormalities, making it a practical tool for resource-limited settings. While not a replacement for professional medical devices, it provides useful preliminary monitoring and can be integrated with remote health platforms for broader healthcare access.

Conclusion

The intelligent patient monitoring system designed in this work effectively illustrates a cost-effective, real-time solution to monitor critical health parameters like blood oxygen saturation, heart rate, body temperature, and ECG signals through integrated sensors and an ESP32 microcontroller. The data processing capability of the system with display on an OLED screen coupled with an alarm facility using a buzzer guarantees prompt feedback under abnormal situations. Experimental testing validated that the system is capable of identifying departures from normal physiological ranges with reasonable accuracy and thus can be used for initial health screening. While not meant to be used as a replacement for medical-grade equipment, the system is useful for application in home care or resource-constrained environments. Modularity provides the potential for future upgrades such as wireless connectivity, cloud integration, and mobile notification to enable remote health monitoring. In general, this study creates an operational, accessible prototype that utilizes embedded technologies and IoT to help address the increasing demand for decentralized healthcare solutions. Acknowledgment

References

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Copyright

Copyright © 2025 Prof. Priya Jamdade, Aryan Mahajan, Navneet Chinchole, Sanchita Sable, Prathamesh Mulukh, Shivam . 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.