Real-Time Intravenous Infusion Monitoring via IOT with Enhanced Accuracy Using MAF

Abstract: This research paper presents the development of an advanced Internet of Things (IOT)-based Intravenous (IV) Infusion Monitoring System, incorporating a Moving Average Filter (MAF) to optimize sensor data accuracy and reliability. The system is designed to facilitate real-time monitoring of essential physiological parameters, including IV fluid level, body temperature, and heart rate, thereby enhancing the scope and efficiency of patient care, particularly in remote or resource-limited settings. By integrating data filtering techniques, specifically the MAF algorithm, the system effectively suppresses random noise and transient fluctuations that typically degrade sensor performance, ensuring more stable and accurate readings. The system are simulated in Proteus, The proposed solution showcases significant potential for remote patient monitoring, smart hospital environments, and home-based healthcare systems, offering a cost-effective, scalable, and technically robust alternative to conventional infusion monitoring methods. The integration of wireless connectivity and real-time data processing further aligns the system with modern telemedicine and healthcare IOT frameworks.

Keywords: IOT, Vital sign Monitoring, Moving Average Filter, Arduino, Proteus, Data Filtering