Cardiopulmonary resuscitation quality parameters from motion capture data using Differential Evolution fitting of sinusoids

TL;DR

This paper introduces a novel method using motion capture data and Differential Evolution to accurately analyze CPR quality parameters, aiming to improve unsupervised CPR training feedback systems.

Contribution

It presents a new approach combining sinusoid models and DE optimization to derive CPR parameters from skeletal motion data, enhancing training feedback accuracy.

Findings

Median error of ±2.9 compressions per minute in frequency detection

Validated approach with 28 participants against a state-of-the-art mannequin

Optimized DE hyperparameters improved parameter recognition accuracy

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