1 research outputs found
Cardiopulmonary resuscitation quality parameters from motion capture data using Differential Evolution fitting of sinusoids
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 compressions per minute compared to the reference training
mannequin.Comment: Final paper, 22 page