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Cardiopulmonary resuscitation quality parameters from motion capture data using Differential Evolution fitting of sinusoids

TitleCardiopulmonary resuscitation quality parameters from motion capture data using Differential Evolution fitting of sinusoids
Publication TypeJournal Article
Year of Publication2019
AuthorsLins C., Eckhoff D., Klausen A., Hellmers S., Hein A., Fudickar S.
JournalApplied Soft Computing
Date Published2019
Publication Languageeng
ISSN1568-4946
KeywordsCardiac arrest, CPR training, Differential Evolution, Kinect, Motion capture, Resuscitation, Sinusoid regression model
Abstract

Cardiopulmonary resuscitation (CPR) is alongside electrical defibrillation the most crucial countermeasure for sudden cardiac arrest, which affects thousands of individuals every year. In this paper, we present a novel approach including sinusoid models that use skeletal motion data from an RGB-D (Kinect) sensor and the Differential Evolution (DE) optimization algorithm to dynamically fit sinusoidal curves to derive frequency and depth parameters for cardiopulmonary resuscitation training. It is intended to be part of a robust and easy-to-use feedback system for CPR training, allowing its use for unsupervised training. The accuracy of this DE-based approach is evaluated in comparison with data of 28 participants recorded by a state-of-the-art training mannequin. We optimized the DE algorithm hyperparameters and showed that with these optimized parameters the frequency of the CPR is recognized with a median error of ±2.9 compressions per minute compared to the reference training mannequin.

URLhttp://www.sciencedirect.com/science/article/pii/S1568494619301413
DOI10.1016/j.asoc.2019.03.023
Created at November 22, 2021 - 2:56pm by Fudickar.

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