# Chest Compression Skill Evaluation System Using Pose Estimation and Web-Based Application
###### Abstract  
It is critical to provide life-sustaining treatment to OHCA patients before ambulance care arrives. However, incorrectly performed resuscitation maneuvers reduce the chances of survival and recovery for the victims. Therefore, we must train regularly and learn how to do it correctly. To facilitate regular chest compression training, this study aims to improve the accuracy of a chest compression evaluation system using posture estimation and to develop a web application. To analyze and enhance accuracy, the YOLOv8 posture estimation was used to examine compression depth, recoil, and tempo, and its accuracy was compared to that of the manikin, which has evaluation systems. We conducted comparative experiments with different camera angles and heights to optimize the accuracy of the evaluation. The experimental results showed that an angle of 30 degrees and a height of 50 cm produced superior accuracy. For web application development, a system has been designed to allow users to upload videos for analysis and obtain appropriate compression parameters. The usability evaluation of the application confirmed its ease of use and accessibility, and positive feedback was obtained. In the conclusion, these findings suggest that optimizing recording conditions significantly improves the accuracy of posture-based chest compression evaluation. Future work will focus on enhancing real-time feedback functionality and improving the user interface of the web application. CPR; OHCA; AED; lifesaving medical care; posture estimation; web application 2020 IEEE/RSJ Web-Based Application  
Among these patients, 58.1% received LMC before the ambulance arrived. Their survival rate after one month was 15.2%, while 10.2% successfully reintegrated into society. These are nearly twice as high as the survival rate for those who did not receive LMC. However, this percentage includes LMC using an AED. In public settings, 92.7% of LMC cases were performed without using an AED. This can be thought of as an Automated External Defibrillator (AED) not being nearby in most cases of OHCA. If not performed correctly, resuscitation maneuvers reduce the chances of survival and recovery for the victims. Therefore, it is necessary for members of the public to acquire CPR skills and maintain these skills through regular training. Recent studies [2] have shown that the chances of surviving OHCA vary significantly depending on the region. For example, [PERSON] et al. reported that the survival rate after OHCA is much lower in Asia compared to Europe and North America [1]. This highlights the importance of accessible and effective training tools to improve CPR skills, especially in areas where survival rates are low. Our study aims to address this issue by developing a system that uses a smartphone to evaluate chest compression techniques. The system provides feedback to help users improve the quality of the CPR they perform. This study aims at two goals to identify an optimal recording environment that ensures an estimate with high accuracy even when using a smartphone and to develop a web application that provides a clear visual representation for easy understanding. The first consideration is the recording environment. If the location for capturing chest compressions is not properly determined, keypoint estimation may be hindered. Second, when evaluating chest compressions and presenting the results, they must be conveyed in a way that is easily understood by the user. The first is to identify an environment that ensures high accuracy. This involves identifying an environment where the camera height and angle can be adjusted to enhance keypoint estimation accuracy when using a smartphone for recording. The second objective is to develop a web-based chest compression evaluation system with a user-friendly interface. We hypothesize that the recording environment significantly impacts the evaluation accuracy of chest compressions in systems that use pose estimation.