IOT-Based Patient Health and Fall Monitoring System

Abstract

In recent years, the demand for continuous and real-time health monitoring solutions has intensified, especially for elderly individuals, chronically ill patients, and those receiving home-based care. Traditional health monitoring methods—largely dependent on manual intervention—are often inefficient, prone to human error, and incapable of providing immediate medical response during emergencies. To overcome these limitations, this study proposes an IoT-based health care monitoring and alert system that integrates low-cost microcontroller technology and a network of biomedical and motion sensors to deliver uninterrupted patient surveillance.The proposed system is designed around the Arduino Uno microcontroller and includes key sensing modules such as a heart rate sensor, SpO? (oxygen saturation) sensor, and a temperature sensor to measure vital signs. An ADXL345 accelerometer is utilized to detect falls, which are a common and critical issue among elderly or disabled individuals. In addition to physiological data monitoring, the system incorporates a GPS module to track the patient\'s real-time location and a GSM module to send SMS alerts to caregivers or medical personnel in case of emergency events—such as abnormal readings or a fall incident. All captured data is visualized on a local LCD display and simultaneously transmitted to a cloud-based IoT platform for remote access by healthcare providers. The integration of these components ensures that patients are constantly monitored and that caregivers are promptly informed of any irregularities. Experimental evaluations demonstrate the system’s capability to provide accurate, real-time data acquisition and transmission, with swift alert responses and robust performance across various test scenarios. The design emphasizes affordability, ease of deployment, and scalability, making it especially beneficial for under-resourced healthcare environments, rural clinics, and in-home care setups..

Introduction

The text discusses the development of an IoT-based health monitoring and alert system designed to provide continuous, real-time tracking of vital signs and fall detection, primarily for elderly, chronically ill, or home-care patients. Traditional manual health monitoring is inefficient and prone to delays in emergencies. Leveraging technologies like Arduino microcontrollers, biomedical sensors (heart rate, SpO?, temperature), an accelerometer for fall detection, GPS for location tracking, and GSM for SMS alerts, the system aims to offer an affordable, reliable, and scalable solution suitable for resource-limited settings.

The system collects data from integrated sensors, processes it, displays information locally, and sends immediate alerts to caregivers during emergencies. It can also upload data to cloud platforms for remote monitoring. Testing showed high accuracy in vital sign measurement and fall detection, with quick alert response times. A home-based case study demonstrated its effectiveness in real-life conditions, validating its potential to improve patient safety and healthcare responsiveness.

Conclusion

This project presents a comprehensive and cost-effective IoT-based health care monitoring system aimed at enhancing patient safety and improving remote care capabilities. By integrating essential biomedical sensors such as heart rate, SpO?, and temperature, along with fall detection, GPS, and GSM communication modules, the system enables continuous, real-time monitoring of patients. The Arduino Uno-based architecture ensures ease of development, affordability, and scalability. Emergency situations such as abnormal health readings or fall incidents trigger instant alerts via SMS along with the patient’s location, ensuring timely intervention. Real-time data is displayed locally through an LCD screen and can be transmitted to a cloud-based IoT dashboard for remote access. This work demonstrates that even with minimal resources, reliable health monitoring solutions can be developed to serve the needs of elderly, chronically ill, or home-care patients.

References

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Copyright

Copyright © 2025 M. Arun, K. Dinesh , M. Sarumathan, R. Santhoshi. 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.