EMP3D: An Emergency Medical Procedures 3D Dataset with Pose and Shape
Hehao Bao1, Keying Du1, Xinyi Su1, Jianqi Fan1, Jing Huang1, Wenlong Dong2, Lu Lu2*, Kun Li1*
1College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
2Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin 300072, China
* Corresponding author
Paper Datasets
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.
Collection
The data collection process for EMP3D is meticulously designed to accurately capture the movements of medical personnel during emergency procedures. Six GoPro cameras are strategically positioned around a simulated medical environment to capture multiple critical perspectives. The video streams from these cameras are synchronized using sound signals, ensuring precise alignment across all six viewpoints. Initially, the 2D posesof six synchronized viewpoints are estimated based on rtmpose and dwpose. Subsequently, joint points across these viewpoints are matched through 4D association to derive 3D joint coordinates. We have created theTracking module to track the positions of the rescuers and patients in each frame. The "tracking" module primarily involves operations such as trajectory initialization, feature vector construction, cost matrix computation, and linear matching. Ultimately, an accurate 3D SMPL-H parameterized model is obtained via a two-stage optimization method.Manual inspections are conducted for both 2D and 3D keypoint results of each frame. Any identifiedissues are manually adjusted to refine the results. Additionally, the 3D SMPL-H results for each frame are also manually inspected. Although the manual inspection process is time-consuming, it ensures a high-quality dataset.
Download
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Data Structure
The EMP3D dataset is structured as follows:
EMP3D
├── CPR
│ ├── yyyymmdd_xxxxxx
│ │ |
│ │ ├── keypoints
│ │ ├── smpl_parameters
│ │ ├── cameras
│ │ ├── videos
│ │ └── synchronization_information
│ └── ...
├── Hemostasis
│ └── ...
├── Bandaging
│ └── ...
├── Heimlich
│ └── ...
└── Fracture_fixation
└── ...
Contact us
If you have any questions or suggestions, please feel free to contact us at: lik@tju.edu.cn. Address: Building 55, Tianjin University Beiyangyuan Campus, 135 Yaguan Road, Haihe Education Park, Jinnan District, Tianjin