Cargando…
Data-driven resuscitation training using pose estimation
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 detecti...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10105636/ https://www.ncbi.nlm.nih.gov/pubmed/37061746 http://dx.doi.org/10.1186/s41077-023-00251-6 |
_version_ | 1785026252856360960 |
---|---|
author | Weiss, Kerrin E. Kolbe, Michaela Nef, Andrina Grande, Bastian Kalirajan, Bravin Meboldt, Mirko Lohmeyer, Quentin |
author_facet | Weiss, Kerrin E. Kolbe, Michaela Nef, Andrina Grande, Bastian Kalirajan, Bravin Meboldt, Mirko Lohmeyer, Quentin |
author_sort | Weiss, Kerrin E. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10105636 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-101056362023-04-17 Data-driven resuscitation training using pose estimation Weiss, Kerrin E. Kolbe, Michaela Nef, Andrina Grande, Bastian Kalirajan, Bravin Meboldt, Mirko Lohmeyer, Quentin Adv Simul (Lond) Methodological Intersections 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. BioMed Central 2023-04-16 /pmc/articles/PMC10105636/ /pubmed/37061746 http://dx.doi.org/10.1186/s41077-023-00251-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Methodological Intersections Weiss, Kerrin E. Kolbe, Michaela Nef, Andrina Grande, Bastian Kalirajan, Bravin Meboldt, Mirko Lohmeyer, Quentin Data-driven resuscitation training using pose estimation |
title | Data-driven resuscitation training using pose estimation |
title_full | Data-driven resuscitation training using pose estimation |
title_fullStr | Data-driven resuscitation training using pose estimation |
title_full_unstemmed | Data-driven resuscitation training using pose estimation |
title_short | Data-driven resuscitation training using pose estimation |
title_sort | data-driven resuscitation training using pose estimation |
topic | Methodological Intersections |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10105636/ https://www.ncbi.nlm.nih.gov/pubmed/37061746 http://dx.doi.org/10.1186/s41077-023-00251-6 |
work_keys_str_mv | AT weisskerrine datadrivenresuscitationtrainingusingposeestimation AT kolbemichaela datadrivenresuscitationtrainingusingposeestimation AT nefandrina datadrivenresuscitationtrainingusingposeestimation AT grandebastian datadrivenresuscitationtrainingusingposeestimation AT kalirajanbravin datadrivenresuscitationtrainingusingposeestimation AT meboldtmirko datadrivenresuscitationtrainingusingposeestimation AT lohmeyerquentin datadrivenresuscitationtrainingusingposeestimation |