Header

UZH-Logo

Maintenance Infos

Data-driven resuscitation training using pose estimation


Weiss, Kerrin E; Kolbe, Michaela; Nef, Andrina; Grande, Bastian; Kalirajan, Bravin; Meboldt, Mirko; Lohmeyer, Quentin (2023). Data-driven resuscitation training using pose estimation. Advances in Simulation, 8(1):12.

Abstract

Background
Cardiopulmonary 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.

Methods
After 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.

Results
The 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.

Conclusions
Pose 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 registration
Not applicable.

Abstract

Background
Cardiopulmonary 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.

Methods
After 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.

Results
The 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.

Conclusions
Pose 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 registration
Not applicable.

Statistics

Citations

Dimensions.ai Metrics
3 citations in Web of Science®
3 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

2 downloads since deposited on 29 Dec 2023
2 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Institute of Anesthesiology
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Social Sciences & Humanities > General Psychology
Life Sciences > Developmental Neuroscience
Life Sciences > Ecology, Evolution, Behavior and Systematics
Social Sciences & Humanities > General Social Sciences
Health Sciences > Psychiatric Mental Health
Life Sciences > Cognitive Neuroscience
Uncontrolled Keywords:General Engineering
Language:English
Date:16 April 2023
Deposited On:29 Dec 2023 12:07
Last Modified:30 Apr 2024 01:45
Publisher:BioMed Central
ISSN:2059-0628
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1186/s41077-023-00251-6
PubMed ID:37061746
Other Identification Number:PMCID: PMC10105636
Project Information:
  • : FunderSwiss Federal Institute of Technology Zurich
  • : Grant ID
  • : Project Title
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)