An IoT-Enabled Wearable Sensor Framework For Early Detection Of Cardiac Arrest

Sudden cardiac arrest is a critical medical emergency that demands immediate recognition and timely intervention to improve a patient's chances of survival. Although hospital-based cardiac monitoring systems are dependable, their high expense and lack of portability make them impractical for everyday personal monitoring. This work presents an Internet of Things (IoT)-based wearable framework that enables continuous and real-time cardiac health observation in non-clinical environments. The device integrates multiple biosensors including electrocardiogram (ECG), pulse oximeter, body temperature, and galvanic skin response (GSR) to acquire physiological data. The acquired data are uploaded to a cloud environment, where algorithms evaluate and categorize the user's cardiac condition as normal, borderline, or severe. The system is linked to a companion mobile application that visualizes real-time readings and automatically issues alerts to caregivers and medical professionals when abnormalities are detected. Through the integration of wearable sensors, edge–cloud data analysis, and IoT communication, the proposed system delivers an economical approach for early cardiac distress prediction and prompt emergency support.