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 skelet al 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 @math compressions per minute compared to the reference training mannequin.
developed and evaluated an augmented reality system that enables the user to correct her or his posture while performing cardiac massage compression @cite_36 . The focus of this work lays on the correct posture of the performer primarily to differentiate between extended position compression and bent position compression. Unfortunately, they have not yet provided a quantitative evaluation of their system.
@cite_36
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In an effort to improve lay rescuers' knowledge and skills in cardiopulmonary resuscitation (CPR), we developed a system that enables high-quality CPR by improving the perceptions of and skills in CPR. The proposed system can evaluate user CPR actions in real time. Particular characteristics of this system are the use of distance and pressure sensors for laypeople to achieve the necessary posture during CPR chest compression and the provision of audiovisual corrective feedback using augmented reality (AR). This system is anticipated to contribute to high-quality CPR training.