Deep Learning-Based IoT System for Remote Monitoring and Early Detection of Health Issues in Real-Time

Abstract With the rise of chronic diseases and an aging population, continuous health monitoring has become crucial for early detection and prevention of critical conditions. Traditional healthcare systems often fail to provide real-time monitoring and timely interventions. This paper presents a deep learning based IoT system for real-time remote health monitoring and early disease detection. The system utilizes wearable IoT sensors to collect physiological data such as heart rate, blood pressure, SpO2, ECG, and body temperature. The collected data is transmitted to an edge/cloud computing system, where deep learning models analyze the patterns to detect abnormalities. The proposed system integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for accurate classification of health conditions. Experimental results demonstrate improved accuracy, sensitivity, and specificity in detecting cardiovascular diseases, respiratory conditions, and other health issues. The system ensures real-time alerts to medical professionals and caregivers, reducing response time and improving patient outcomes.