A novel algorithm of fast CPR quality evaluation based on kinect

Abstract

Human action recognition is a very popular field in computer vision research, and the research results are widely used in people's lives. This paper explores Kinect-based algorithm of human action recognition and applies it to the quality evaluation of cardiopulmonary resuscitation (CPR) operation. At present, the main means of CRP training is through physical auxiliary equipment, which has a large limitation and can only be carried out under specific conditions. CPR simulation training under general conditions can be effectively carried out by means of computer vision, which is a strategy worth popularizing. Using Kinect's powerful skeleton tracking capabilities to obtain key human skeleton data and then perform fine-grained human action analysis. Our model can obtain the critical compression depth (CCD) and compression frequency (CCF) of CPR. Compared with the-state-of-the-art, our algorithm has better stability and real-time performance. At the same time, our algorithm improves the time efficiency by about 60% while guaranteeing high accuracy. In addition, we guide the human body to perform standard movements by setting joint angle specifications.Moreover, our system has been proven to be valid by professional medical staff.