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.
Abstract Background Chest compression (CC) quality is associated with rescuer posture and body weight. We designed a Kinect module-based real-time audiovisual feedback (AVF) device to investigate the relationship between rescuer posture, body weight, and CC quality. Methods A total of 100 healthcare professionals were enrolled as participants in this randomized trial. A Kinect-based sensor system was used to monitor the depth and rate of CC and provide further real-time feedback. All participants were asked to perform continuous CC on a manikin with and without feedback for 2 min individually in either a kneeling or standing position. Results A kneeling posture can provide higher rate of CC than a standing posture can (111.4 ± 22.6 per minute vs. 99.1 ± 18.9 per minute, p value = 0.005). Real-time AVF feedback can provide a better compression depth, rate, and effective compression ratio (6.16 ± 1.88 cm vs. 5.54 ± 1.89 cm, p value = 0.02; 103.2 ± 21.0 min vs. 96.7 ± 25.8 min, p value = 0.03; 62.6 ± 28.0 vs. 51.0 ± 33.2 , p value = 0.004). Regardless of the effect of real-time feedback, the CC depth correlated to the rescuers' body weight. Rescuers who weighed below 71 kg benefited from the Kinect module-based real-time AVF device in terms of improved CC quality. Conclusion The Kinect-based AVF device can significantly improve CC quality in manikin training in rescuers with their body weight