Edge Computing Based Cardiac Monitoring and Detection System in the Internet of Medical Things

Currently, cardiac disease becomes one of the major health issues in the world. The death due to cardiac arrests has been increasing day by day. The survival rate for the sudden cardiac arrest is low. It is possible to save 60% human life by continuously monitoring the fitness of the person. In recent times, the Internet of Things is emerging promising technologies. If the doctor cannot monitor the patient face to face, there Internet of Things plays important role in remotely monitoring the patient’s condition. With the rise in the use of smart wearable gadgets, the contribution of IoT will be more. In this paper, about five predictor model are analyzed for a particular dataset and chosen the best as Random forest algorithm. The proposed system integrates oxygen saturation level and heart rate sensor data from MAX30100 sensor for monitoring the patient’s vital signs. The remaining parametric data of the patient (AGE, GENDER, BLOOD SUGAR LEVEL, BLOOD PRESSURE LEVEL, GENETIC ISSUE and CHLOESTEROL LEVEL) are fed through the matrix keypad. The designed system monitors the online patient’s vitals continuously. The patient’s vitals will be stored in the edge server and also analyzed. The chosen predictor model checks the vital data for any abnormality and based on abnormality data detection in the heart rate, the warning message is conveyed to the nearby health care center via Thingspeak cloud. By tracking the patient’s GPS location, the server cloud sends alert notification along with the GPS location to the nearby health center and to the registered mobile numbers.