Life-Line: Real-Time Patient Health Monitoring System

Abstract

The system\'s main purpose is to monitor basic health parameters like heart rate, body temperature, blood oxygen: SpO2, and blood pressure on a continuous basis. It aims to build a cost-effective health monitoring system for individuals in remote areas which do not have access to specialist doctors. The System is portable and can be operated by a lay-man with minimal training. This is achieved through wearable sensors and IoT technologies that collect data, transmit it, and facilitate real-time processing via a cloud platform. The Users have access to a mobile or web interface where they receive real-time updates and are able to monitor their health at any given time. The system is also useful for healthcare professionals because they are provided with remote access to patient data which allows for continuous observation and timely interventions. With the integration of Wi-Fi, Bluetooth, and GSM, seamless data transfer is guaranteed, in addition to expandable cloud storage that supports scalable data analysis for supplemental security. Apart from real-time monitoring, the system also employs predictive data analytics and machine learning algorithms to identify irregularities that may indicate risk of potential health issues. When abnormal patterns are detected, the system allows for automated alerts and notifications to be sent which enable proactive response to such situations.

Introduction

With rising chronic diseases demanding continuous health oversight, traditional healthcare’s periodic monitoring falls short in timely detection and management. To address these gaps, the proposed IoT-based Real-Time Patient Health Monitoring System uses wearable sensors and cloud computing to continuously collect and analyze vital signs like heart rate, temperature, and oxygen levels.

Existing systems rely heavily on manual monitoring and face challenges such as data privacy, connectivity, and scalability. The proposed solution integrates sensors (MAX30100 pulse oximeter, DHT11 temperature sensor), an Arduino Nano microcontroller, and Wi-Fi modules to transmit real-time data to mobile apps and cloud platforms. Alerts are automatically generated when abnormal readings occur, notifying caregivers and hospitals promptly.

The system architecture includes sensor data collection, wireless transmission, cloud storage, and user interfaces for real-time monitoring. Testing confirmed high accuracy (less than 2% error), reliability (98% uptime), and quick data transmission (0.5-1 second latency). Compared to existing methods, this system offers continuous monitoring, portability, immediate emergency alerts, affordability, and advanced data analytics.

Key features include secure data transmission, real-time alerts, predictive analytics, and a user-friendly interface. Future enhancements envision AI integration for predictive insights, advanced sensors for broader health metrics, improved wearable designs, interoperability with healthcare records, and edge computing for faster local processing.

Conclusion

The LIFE-LINE: Real-Time Patient Health Monitoring System proved to be an effective and reliable solution for continuous health monitoring in medical and remote settings. The system demonstrated high accuracy in vital sign data collection, with minimal latency, making it suitable for real-time healthcare applications. Its ability to scale and its energy-efficient design ensure that it can handle long-term monitoring for multiple patients, enhancing both individual and public health management. While the system’s performance meets current healthcare standards, the future enhancements, such as AI integration, advanced sensors, and improved wearables, will further elevate its capabilities, ensuring a more comprehensive, proactive, and secure approach to patient care. This system shows significant promise in revolutionizing patient monitoring, making it a valuable tool for healthcare providers in improving patient outcomes and ensuring timely interventions.

References

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Copyright

Copyright © 2025 Mithra Siliveru. 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.