# Quality Evaluation Algorithm for Chest Compressions Based on OpenPose Model
## 1 Introduction  
In recent years, cardiac arrest (CA) has had an extremely high morbidity and mortality rate worldwide [1]. CPR is the most effective means of rescuing patients with Outside of Hospital Cardiac arrest (OHCA) [2]. Therefore, it is very urgent to increase the popularity of CPR among the public. A complete CPR process includes five parts: on-site environmental safety assessment, patient injury assessment and calling for help, CCs, artificial respiration and defibrillation. For CPR procedures, CCs have the most extended duration [3]. Interruption of compressions, unstable compression speed, and intensity can all affect the effectiveness of CCs, resulting in poor resuscitation quality and low patient survival [4]. Effective and continuous CCs can improve coronary artery perfusion and myocardial ischemia and hypoxia [5, 6]. Therefore, for the training of CPR, standard action training and assessment should be the main objective.  
At present, the most effective method in the traditional CPR training mode is to have trainees perform CCs on CPR manikins [7], as shown in Figure 1. The manikin, which is equipped with various sensors, can provide feedback on compression depth, frequency and whether the chest fully rebounds during the operation. However, it cannot monitor the operating position and whether the elbow is straight, which is essential to ensure CC quality. Therefore, monitoring elbow straightness and operating position is generally the instructor's work. However, one instructor can only train six people simultaneously at most, which is inefficient and unable to guarantee the quality of the supervision. Therefore, the automatic judgment of CCs is an essential step in realizing innovative CPR training and evaluation.  
As the name suggests, the aim of HPE [8] is to locate key points (such as the head, right hand, and left shoulder) of the human body from an image or video and to track the pose. With the development of deep learning, HPE is applied in a wide range of fields, such as virtual reality, sports motion analysis, human-computer interactions and medical health care.  
In this paper we introduce an intelligent approach for evaluating CC quality using video imagery. OpenPose [9] is applied to the evaluation process in CPR. The recognition algorithm is realized based on the OpenPose network by extracting the coordinates of key points from video frames recorded by monocular cameras, preprocessing the data and using the joint point coordinates for corresponding calculations. Since the key indicators (depth, frequency, position, chest rebound and elbow straightness) can be calculated from the coordinate data, the operator does not need to install any sensors in the manikin body. Based on the rules of CPR guidelines from the American Heart Association (AHA) [10], the European Resuscitation Council (ERC) [11], the European Society of Cardiology (ESC) [12] and the Extracorporeal Life Support Organization (ELSO) [13], and based on our research on compression poses, the algorithm finally makes an assessment. The first section introduces the system architecture and the OpenPose model. Then, we conduct research to define reasonable CC criteria. In the second section, we give a detailed introduction to algorithm implementation based on the principles mentioned above. Finally, we design an experiment to prove the effectiveness of the method.