@article{lins2019_cprkinect,Author = {Lins, Christian and Eckhoff, Daniel and Klausen, Andreas and Hellmers, Sandra and Hein, Andreas and Fudickar, Sebastian},Title = {Cardiopulmonary resuscitation quality parameters from motion capture data using Differential Evolution fitting of sinusoids},Journal = {Applied Soft Computing},Year = {2019},Pages = {300-309},Month = {06},Publisher = {Elsevier B.V.},Doi = {10.1016/j.asoc.2019.03.023},Url = {https://www.sciencedirect.com/science/article/pii/S1568494619301413?dgcid=author},type = {article},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.}}@COMMENT{Bibtex file generated on }