CPR-Coach: Recognizing Composite Error Actions Based on Single-Class Training

Fine- grained medical action analysis plays a vital role in improving medical skill training efficiency, but it faces the problems of data and algorithm shortage. Cardiopul-monary Resuscitation (CPR) is an essential skill in emer-gency 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, this pa-per constructs 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 compres-sion and then develop a video dataset named CPR-Coach. By taking the CPR-Coach as a benchmark, this paper in-vestigates and compares 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 named ImagineNet to improve the model's multi-error recognition performance under restricted supervision. Extensive comparison and actual deployment experiments verify the effectiveness of the framework. We hope this work could bring new inspiration to the computer vision and medical skills training communities simultaneously. The dataset and the code are publicly available on https://github.com/Shunli-Wang/CPR-Coach.