  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 $\pm 2.9$ compressions per minute compared to the reference training
mannequin.
