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.
Cite
@article{arxiv.1806.10115,
title = {Cardiopulmonary resuscitation quality parameters from motion capture data using Differential Evolution fitting of sinusoids},
author = {Christian Lins and Daniel Eckhoff and Andreas Klausen and Sandra Hellmers and Andreas Hein and Sebastian Fudickar},
journal= {arXiv preprint arXiv:1806.10115},
year = {2020}
}