Cardiopulmonary resuscitation quality parameters from motion capture data using Differential Evolution fitting of sinusoids
This work addresses the need for robust and easy-to-use feedback systems for CPR training, enabling unsupervised practice, though it is incremental as it applies an existing optimization method to a specific domain.
The paper tackled the problem of estimating CPR quality parameters (frequency and depth) from motion capture data by using a Differential Evolution algorithm to fit sinusoids, achieving a median error of ±2.9 compressions per minute for frequency compared to a reference mannequin.
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.