CPR-Coach: Recognizing Composite Error Actions based on Single-class Training
This work addresses the high cost and inefficiency of CPR skill assessment by providing a dataset and method for automated error recognition, though it is incremental as it builds on existing action recognition models.
The paper tackles the problem of recognizing composite error actions in Cardiopulmonary Resuscitation (CPR) training by constructing a vision-based system and a dataset called CPR-Coach, proposing the ImagineNet framework to improve multi-error recognition under single-class training constraints, with extensive experiments verifying its effectiveness.
The fine-grained medical action analysis task has received considerable attention from pattern recognition communities recently, but it faces the problems of data and algorithm shortage. 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, this paper 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 compression and then develop a video dataset named CPR-Coach. By taking the CPR-Coach as a benchmark, this paper thoroughly investigates 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 humancognition-inspired framework named ImagineNet to improve the model's multierror recognition performance under restricted supervision. Extensive experiments verify the effectiveness of the framework. We hope this work could advance research toward fine-grained medical action analysis and skill assessment. The CPR-Coach dataset and the code of ImagineNet are publicly available on Github.