<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lins, Christian</style></author><author><style face="normal" font="default" size="100%">Eckhoff, Daniel</style></author><author><style face="normal" font="default" size="100%">Klausen, Andreas</style></author><author><style face="normal" font="default" size="100%">Hellmers, Sandra</style></author><author><style face="normal" font="default" size="100%">Hein, Andreas</style></author><author><style face="normal" font="default" size="100%">Fudickar, Sebastian</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cardiopulmonary resuscitation quality parameters from motion capture data using Differential Evolution fitting of sinusoids</style></title><secondary-title><style face="normal" font="default" size="100%">Applied Soft Computing</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cardiac arrest</style></keyword><keyword><style  face="normal" font="default" size="100%">CPR training</style></keyword><keyword><style  face="normal" font="default" size="100%">Differential Evolution</style></keyword><keyword><style  face="normal" font="default" size="100%">Kinect</style></keyword><keyword><style  face="normal" font="default" size="100%">Motion capture</style></keyword><keyword><style  face="normal" font="default" size="100%">Resuscitation</style></keyword><keyword><style  face="normal" font="default" size="100%">Sinusoid regression model</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S1568494619301413</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record></records></xml>