Intelligent Healthcare Monitoring System Using IoT and Machine Learning for Real-Time Analytics and Prediction
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Keywords
Smart health monitoring, Internet of Things (IoT), wearable sensors, ESP32, Raspberry Pi, machine learning, healthcare monitoring.
Abstract
As wearable technology and Internet of Things (IoT) advances, real-time health monitors are becoming an integral product in contemporary healthcare. Learn how constant patient monitoring can lead to quicker intervention for serious medical problems. This paper proposes a Smart Health Monitoring System using various sensors, microcontroller and machine learning algorithms to analyze patients’ health data in real time. The system utilizes sensors to record physiological parameters such as heart rate, body temperature, and blood oxygen saturation (SpO₂). A data processing which is done in ESP32 microcontroller and the results are wirelessly sent to Raspberry Pi for further analysis. Raspberry Pi comes with a machine learning model that processes incoming data to detect abnormal health conditions and invoke alerts accordingly. This system facilitates continuous monitoring and effective data analysis, which in turn assists medical professionals with timely diagnosis. It is low-cost, scalable and applicable to the hospital environment as well as remote patient monitoring.