A Wireless Early Prediction System of Cardiac Arrest through IoT

The increase in popularity for wearable technologies has opened the door for an Internet of Things (IoT) solution to healthcare. One of the most prevalent healthcare problems today is the poor survival rate of out-of-hospital sudden cardiac arrests. The objective of this study is to present a multisensory system using IoT that can collect physical activity heart rates and body temperature. For this study, we implemented an embedded sensory system with a Low Energy (LE) Bluetooth communication module to collect ECG and body temperature data using a smartphone in a common environment. This study introduces the use of signal processing and machine learning techniques for sensor data analytics for sudden cardiac arrest and or heart attack prediction.