CPR-Coach: Recognizing Composite Error Actions based on Single-class
  Training
Shunli Wang Qing Yu Shuaibing Wang Dingkang Yang Liuzhen Su Xiao Zhao Haopeng Kuang Peixuan Zhang Peng Zhai Lihua Zhang
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.