Enhanced Real-Time Analysis and Anomaly Detection in Smart Health Monitoring Systems through Integration of Deep Learning Algorithm with IoT-Edge Computing

In this research, the proposed system HealthEdge, a new IoT-based intelligent health monitoring framework based on edge computing and a diverse sensor array that includes breathing frequency (RR), pulse rate (PR), oxygen saturation level in blood (SpO2), galvanic skin response (GSR), body temperature and hemoencephalography as vital signs sensors, as well as a formaldehyde sensor to gauge environmental influence on health. HealthEdge utilizes a powerful Deep Convolutional Neural Network (DCNN) to perform and analyze the data in real-time at the edge, ensuring ultra-low latency and amplified privacy. The model is capable of identifying health anomalies with a high accuracy of 97% which is demonstrating its potential in real-time health observation and anomaly detection. The potential role of environmental quality measurements in particular formaldehyde levels, in imposing on health conditions, thereby spreading the purview of the monitoring system beyond the traditional set of parameters have been explored. The integration of edge computing not only addresses the challenges that conventional systems face in maintaining a balance between privacy concerns and processing speed, it is also a state-of-the-art technology which enables HealthEdge to become the new norm for complete and real-time health monitoring solutions.