Automated Health Monitoring: Integrating AI and IoT for Continuous Patient Observation

Integrating Artificial Intelligence (AI) with the Internet of Things (IoT) has recently become more advantageous for various complex fields like innovative health, intrusion detection in various devices, and continuous monitoring of crops yielding in various farming lands. From the health point of view, it is essential to monitor multiple patients' health according to their abnormal health conditions. The proposed Integrated Health Monitoring System (IHMS) is developed in this work by combining AI-based models such as Recurrent Neural Networks (RNNs) and advanced IoT sensors. The IHMS is mainly used in health care applications such as heart care analysis and other abnormal health conditions like blood pressure, levels of oxygen, and glucose readings, and it transmits this information to AI-powered systems. The IHMS mainly analyzes abnormal patterns, detects anomalies, and provides timely analytical analysis of patient data. Finally, the proposed approach IHMS analyzes several datasets and shows the performance in terms of classification.