EMP3D: An Emergency Medical Procedures 3D Dataset with Pose and Shape

Kun Li

Abstract: Emergency Medical Services (EMS) play a critical role in acute emergencies, yet their effectiveness is often limited by professional complexities. For example, a European study on out-of-hospital cardiac arrest (OHCA) found survival rates below 10%, primarily due to delayed responses and insufficient bystander intervention. Existing datasets for medical movement analysis have largely focused on basic patient actions like lying and standing. The NTU dataset includes 2D joint data for actions such as sneezing and covering the head in everyday sickness scenarios. In daily life scenarios uses motion recognition technology to monitor patients’ postures, and in hospital environments uses LiDAR for human posture and motion recognition. However, there is a significant gap in research on the actions of rescuers in medical emergency procedures. Creating a 3D dataset of medical emergency procedures can provide data support for the analysis or generation of emergency medical procedures, thereby facilitating the dissemination of emergency medical practices.