ArticleAdvances in simulation (London, England)2023
Data-driven resuscitation training using pose estimation.
Article in Advances in simulation (London, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A function-based framework for AI-amplified, data-driven healthcare simulation research.Advances in simulation (London, England) · 2026Article
- Computer Vision-Guided Automated External Defibrillator System Improves Cardiopulmonary Resuscitation Accuracy: A Randomized Crossover Trial.Health science reports · 2026Article
- Advancing healthcare practice and education via data sharing: demonstrating the utility of open data by training an artificial intelligence model to assess cardiopulmonary resuscitation skills.Advances in health sciences education : theory and practice · 2025Article
- Feasibility of real-time compression frequency and compression depth assessment in CPR using a "machine-learning" artificial intelligence tool.Resuscitation plus · 2024Article
- Detection and Evaluation for High-Quality Cardiopulmonary Resuscitation Based on a Three-Dimensional Motion Capture System: A Feasibility Study.Sensors (Basel, Switzerland) · 2024Article
- Measuring teamwork for training in healthcare using eye tracking and pose estimation.Frontiers in psychology · 2023Article
- How to enrich team research in healthcare by considering five theoretical perspectives.Frontiers in psychology · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundCardiopulmonary resuscitation (CPR) training improves CPR skills while heavily relying on feedback. The quality of feedback can vary between experts, indicating a need for data-driven feedback to support experts. The goal of this study was to investigate pose estimation, a motion detection technology, to assess individual and team CPR quality with the arm angle and chest-to-chest distance metrics.
methodsAfter mandatory basic life support training, 91 healthcare providers performed a simulated CPR scenario in teams. Their behaviour was simultaneously rated based on pose estimation and by experts. It was assessed if the arm was straight at the elbow, by calculating the mean arm angle, and how close the distance between the team members was during chest compressions, by calculating the chest-to-chest distance. Both pose estimation metrics were compared with the expert ratings.
resultsThe data-driven and expert-based ratings for the arm angle differed by 77.3%, and based on pose estimation, 13.2% of participants kept the arm straight. The chest-to-chest distance ratings by expert and by pose estimation differed by 20.7% and based on pose estimation 63.2% of participants were closer than 1 m to the team member performing compressions.
conclusionsPose estimation-based metrics assessed learners' arm angles in more detail and their chest-to-chest distance comparably to expert ratings. Pose estimation metrics can complement educators with additional objective detail and allow them to focus on other aspects of the simulated CPR training, increasing the training's success and the participants' CPR quality.
trial registrationNot applicable.
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