International Journal of Science, Engineering and TechnologyAuthors: G Thamarai Selvan
Abstract: Stroke is a serious medical condition that often leads to longterm disability or even death if not identified early. Continuous monitoring of vital health parameters can help detect early warning signs and reduce the severity of such conditions. In this study, a smart healthcare system is designed using Internet of Things (IoT) technology with ai to monitor multiple physiological signals in real time. The system uses wearable sensors to measure heart rate, blood oxygen level (SpO₂), body temperature, and body movement. These values are collected using an ESP32 microcontroller and sent to a cloud platform for storage and monitoring. A machine learning model based on the Random Forest algorithm is used to analyze the collected data and classify the risk level into safe, moderate, or high. In addition, the system provides immediate alerts using a buzzer, LED indicators, and mobile notifications whenever abnormal readings are detected. The results show that the model performs with good accuracy and can help in early identification of stroke-related risks. This system is affordable, easy to use, and suitable for remote healthcare monitoring.
DOI: http://doi.org/10.5281/zenodo.21351006
