RÉSUMÉ
Objective:Correct chest compression posture (CCP) is an important basis for high-quality cardiopulmonary resuscitation, but the research on CCP was still very limited. In this study, a new automatic analysis model was developed to achieve the purpose of objectification, standardization and automation of CCP monitoring.Methods:A total of 15 participants, including 11 professionals and 4 nonprofessionals, were recruited to participate in the field experiment. The video data were recorded simultaneously with zed cameras in the front and 45-degree sides. All participants performed 120 consecutive external chest compression operations on the Smartman CPR simulator. Three experts annotated the videos independently. An intelligent algorithm was used to extract human bone points for subsequent analysis and model development. The chi-square test was used to compare the rates of the professional and nonprofessional groups.Results:The results showed that problems with wrists, fingers, center of body weight and elbow bending had the highest incidence. Through 28 800 sets of standard human skeleton point coordinate data, we obtained a reasonable range of arm angles of 169.24°- 180.00° for the left arm and 168.49°-180.00° for the right arm. By the same method, the reasonable range of the center of gravity angle is 0.00°-18.46°. Based on these results, a new chest compression posture detection model based on a dual ZED camera was developed, which can accurately identify CCP errors (accuracy 91.31%; sensitivity 80.16%; specificity 93.53%).Conclusions:This study innovatively proposed an objective evaluation method for CCP. Moreover, a new chest compression posture detection model based on a dual ZED camera was developed, which can accurately identify CCP errors to achieve automation and standardization of quality control in CPR training.