Harnessing optical flow in deep learning framework for cardiopulmonary resuscitation training

Abstract

With the incidence of out-of-hospital cardiac arrest increasing on a global scale, cardiopulmonary resuscitation (CPR) has become crucial in maximizing the survival rate of cardiac arrest patients. However, the feasibility of widespread CPR training remains limited at present, in part owing to the high costs required to equip manikins with feedback functions. We therefore propose a deep learning technology designed to provide a low-cost CPR education. Our model converts chest compression (CC) videos captured via smartphone into appropriate images, and provides feedback on four core components of high-quality CPR: CC count, maximum compression depth of CCs, complete chest release following compression, and incorrect hand positions of CCs. To reduce the complexity of video processing, we conducted a composite-image-based evaluation for time-independent tasks. Our proposed architecture achieved satisfactory performance on all tasks, demonstrating high cost-effectiveness. Moreover, by focusing on educational context, our approach helps expand educational opportunities and enhance CPR training. By allowing many individuals to simultaneously receive CPR training in an optimized educational environment, we can ultimately improve the survival rate of cardiac arrest patients.