Edge Artificial Intelligence-Based Facial Pain Recognition During Myocardial Infarction

Abstract

Medical history highlights that Myocardial Infarction is one of the leading factors of death in human beings. Angina pectoris is a prominent vital sign of Myocardial Infraction. Medical reports suggests that experiencing a chest pain during heart attacks causes changes in facial muscles resulting in variations in patterns of facial expression. The present work intends to develop an automatic facial expression detection to identify the severity of chest pain as a vital signs of MI using algorithmic approach that is implemented with a state of art Convolution Neural Networks CNN. The advanced object detection light weight Convolutional Neural Network models: Single Shot Detector Mobile Net V2 and Single Shot Detector Inception V2 are utilized for designing the vital signs MI model from 500 Red Blue Green Color images private dataset. The authors develop a cardiac emergency health monitoring care using an Edge Artificial Intelligence, “Edge AI” using NVIDIA’s Jetson Nano embedded GPU platform. The proposed model is mainly focused on the low cost and less power consumption factors for onboard real time detection of vital signs of Myocardial infarction. The evaluated metrics achieves a mean Average Precision of 85.18%, Average Recall of 88.32% and 6.85 frames per second for the generated detections.