Integrating IoT and Machine Learning for Real-Time Patient Health Monitoring with Sensor Networks

An innovative approach for continuous health monitoring in medical applications is presented in this research. The proposed system is composed of Raspberry Pi, cloud storage, machine learning, and IoT sensor. The IoT sensor monitors patients' vitals in real time and quickly identifies any anomalies. The patient wearing the sensors transmit the real-time data with Raspberry Pi processors. The Raspberry Pi collects the real time data from sensors such temperature, blood pressure, heart rate, and pulse oximeter. Then the IoT transmits the data collected to a cloud server. K-Nearest Neighbors (KNN) is a data processing and analysis method used in the cloud server. The KNN algorithm categorizes and analyzes the data collected, discovers the trend and anomalies present in the patient's vital signs. The proposed system has a simple user interface that can be accessed via a web or mobile application, allowing doctors and nurses to remotely look at the patient's data and generate real-time alerts in case of severe health situations. While cloud technology ensures scalability, data storage, and advanced analytics, the integration of Raspberry Pi devices makes it possible to process data locally and reduce latency.