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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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Author(s)

  • Christian Lins
  • Andreas Klausen
  • Sandra Hellmers
  • Andreas Hein
  • Sebastian Fudickar

Detail(s)

Original languageEnglish
Pages (from-to)300-309
Number of pages10
Journal / PublicationApplied Soft Computing Journal
Volume79
Online published19 Mar 2019
Publication statusPublished - Jun 2019
Externally publishedYes

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.

Research Area(s)

  • CPR training, Cardiac arrest, Differential Evolution, Kinect, Motion capture, Resuscitation, Sinusoid regression model

Citation Format(s)

Cardiopulmonary resuscitation quality parameters from motion capture data using Differential Evolution fitting of sinusoids. / Lins, Christian; Eckhoff, Daniel; Klausen, Andreas; Hellmers, Sandra; Hein, Andreas; Fudickar, Sebastian.

In: Applied Soft Computing Journal, Vol. 79, 06.2019, p. 300-309.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal