IoT- Edge Deep Learning EHealth Monitoring System

This research aims to investigate the Possibility with viability of open-source Internet of Things (IoT) and edge-compatible equipment in the field of health monitoring. The focus is on exploring various IoT health monitoring environments used in e-health applications, taking into account crucial aspects such as sensor integration, data collection methods, communication protocols, security measures, scalability, and regulatory requirements. The research begins by examining existing IoT health monitoring environments to gain a comprehensive understanding of their strengths and limitations. This analysis helps identify the gaps and challenges that open-source IoT and edge devices can address in the context of health monitoring. Building upon this groundwork, a novel IoT-edge-powered deep learning system will be developed specifically for a targeted health monitoring environment. The system will leverage the capabilities of IoT devices, integrate various sensors to capture relevant health data, harness edge computing techniques to process data locally, and utilize deep learning algorithms for advanced analysis and inference. Special emphasis will be placed on optimizing data preprocessing, feature extraction, model training, and overall system performance. To assess the effectiveness of the proposed IoT-edge-deep learning environment, A comparison with currently implemented