Cardiopulmonary Resuscitation (CPR) is an essential skill in emergency treatment. Currently, the assessment of CPR skills mainly depends on dummies and trainers, leading to high training costs and low efficiency. For the first time, we constructed a vision-based system to complete error action recognition and skill assessment in CPR. Specifically, we define 13 types of single-error actions and 74 types of composite error actions during external cardiac compression and then develop a video dataset. By taking the dataset as a benchmark, we thoroughly investigated and compared the performance of existing action recognition models based on different data modalities. To solve the unavoidable Single-class Training & Multi-class Testing problem, we propose a human-cognition-inspired framework to improve the model's multi-error recognition performance under restricted supervision. We hope this work could advance research toward fine-grained medical action analysis and skill assessment.
Our composite error action recognition system was received as a Demo by ICCV-2023. The detailed system demonstration video is available at Here.
This work was supported by the National Key R&D Program of China (2021ZD0113502) and the Shanghai Municipal Science and Technology Major Project (2021SHZDZX0103).