Prompt-enhanced Hierarchical Transformer Elevating Cardiopulmonary
  Resuscitation Instruction via Temporal Action Segmentation
Yang Liu Xiaoyun Zhong Shiyao Zhai Zhicheng Du Zhenyuan Gao Qiming Huang Canyang Zhang Bin Jiang Vijay Kumar Pandey Sanyang Han Runming Wang Yuxing Han Peiwu Qin
The vast majority of people who suffer unexpected cardiac arrest are
performed cardiopulmonary resuscitation (CPR) by passersby in a desperate
attempt to restore life, but endeavors turn out to be fruitless on account of
disqualification. Fortunately, many pieces of research manifest that
disciplined training will help to elevate the success rate of resuscitation,
which constantly desires a seamless combination of novel techniques to yield
further advancement. To this end, we collect a custom CPR video dataset in
which trainees make efforts to behave resuscitation on mannequins independently
in adherence to approved guidelines, thereby devising an auxiliary toolbox to
assist supervision and rectification of intermediate potential issues via
modern deep learning methodologies. Our research empirically views this problem
as a temporal action segmentation (TAS) task in computer vision, which aims to
segment an untrimmed video at a frame-wise level. Here, we propose a
Prompt-enhanced hierarchical Transformer (PhiTrans) that integrates three
indispensable modules, including a textual prompt-based Video Features
Extractor (VFE), a transformer-based Action Segmentation Executor (ASE), and a
regression-based Prediction Refinement Calibrator (PRC). The backbone of the
model preferentially derives from applications in three approved public
datasets (GTEA, 50Salads, and Breakfast) collected for TAS tasks, which
accounts for the excavation of the segmentation pipeline on the CPR dataset. In
general, we unprecedentedly probe into a feasible pipeline that genuinely
elevates the CPR instruction qualification via action segmentation in
conjunction with cutting-edge deep learning techniques. Associated experiments
advocate our implementation with multiple metrics surpassing 91.0%.