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.
propose a real-time feedback system for CPR training, using the sensor @cite_38 . The system shows the current compression depth and frequency on a computer screen so that the trainee can adapt her or his actions accordingly. By evaluating the system with 100 healthcare professionals, the authors have shown that the system has significant effects on the CPR quality. Their system requires a marker on the trainee's hand and the chest of the training mannequin. Additionally, the placement of the Kinect must be carefully chosen here as they directly derive the compression depth from the sensor data.
@cite_38
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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