Smart Health IoT Monitoring System for Real-Time Arrhythmia Detection Using AI

In today's healthcare environment, keeping an eye on one's health is essential. Convolutional neural networks (CNNs) and real-time health monitoring are used in this paper's IoT-based cardiac arrhythmia diagnosis system to enhance patient care. The method employs a CNN model that has previously been trained on a dataset that includes both atrial fibrillation and normal cardiac events in order to accurately detect arrhythmias. The ESP32 microprocessor, which powers the model, features multiple sensors for body temperature, heart rate, SpO2, and real-time ECG signal gathering. By utilizing the ESP32's Wi-Fi capabilities, healthcare professionals can process and transmit real-time data to a cloud platform, allowing for remote patient health monitoring. Doctors and caregivers can view health metrics in real time and receive notifications when there are irregularities through a smartphone application. The CNN model automatically alerts the authorized healthcare provider when it detects an arrhythmia based on ECG data. This system will be very useful for patients who are in need of real time monitoring of their health.