Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System

TLDR

Smart health monitoring systems, driven by IoT and AI, have become increasingly important, especially during COVID‑19, as they enable remote data collection and decision support for patients. This study aims to design an AI‑powered IoT health monitoring system for heart patients. The system tracks patients’ activities, provides health status feedback, and uses machine‑learning models to classify cardiac diseases. Experimental results show that the system achieves real‑time monitoring and higher disease‑classification accuracy.

Abstract

With technological advancements, smart health monitoring systems are gaining growing importance and popularity. Today, business trends are changing from physical infrastructure to online services. With the restrictions imposed during COVID-19, medical services have been changed. The concepts of smart homes, smart appliances, and smart medical systems have gained popularity. The Internet of Things (IoT) has revolutionized communication and data collection by incorporating smart sensors for data collection from diverse sources. In addition, it utilizes artificial intelligence (AI) approaches to control a large volume of data for better use, storing, managing, and making decisions. In this research, a health monitoring system based on AI and IoT is designed to deal with the data of heart patients. The system monitors the heart patient’s activities, which helps to inform patients about their health status. Moreover, the system can perform disease classification using machine learning models. Experimental results reveal that the proposed system can perform real-time monitoring of patients and classify diseases with higher accuracy.