System and Method for Training and Assessing Cardiopulmonary Resuscitation Performance Based on Feedback

Abstract

A system and method for training, assessing, and providing feedback on cardiopulmonary resuscitation (CPR) performance based on at least one video of a CPR training session performed by a trainee on a non-mannequin training object. A preprocessing module is configured to process the at least one video to generate a standardized video. A marking module is configured to use pose estimation to mark points for body movements during the CPR training session based on the standardized video, and a computing module configured to compute body movement parameters for CPR based on the marked points. A classification module implements a machine learning model that classifies CPR compressions on the non-mannequin training object based on the computed body movement parameters, thereby generating compression classifications, wherein the machine learning model is trained to extract CPR-specific features. An editor module maps metrics over the standardized video based on the compression classifications and generates a feedback video based on the mapped metrics. An analysis module identifies deviations from CPR guidelines based on the feedback video and generates analysis results based on the deviations. A feedback module provides performance feedback to the trainee based on the analysis results.

Dependencies are inferred from the claim text.