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
2170888427 2756177572 2031561585 2774356665
Abstract Introduction Outcome after cardiac arrest is dependent on the quality of chest compressions (CC). A great number of devices have been developed to provide guidance during CPR. The present study evaluates a new CPR feedback system (Mini-VREM: Mini-Virtual Reality Enhanced Mannequin) designed to improve CC during training. Methods Mini-VREM system consists of a Kinect ® (Microsoft, Redmond, WA, USA) motion sensing device and specifically developed software to provide audio–visual feedback. Mini-VREM was connected to a commercially available mannequin (Laerdal Medical, Stavanger, Norway). Eighty trainees (healthcare professionals and lay people) volunteered in this randomised crossover pilot study. All subjects performed a 2 min CC trial, 1 h pause and a second 2 min CC trial. The first group (FB NFB, n = 40) performed CC with Mini-VREM feedback (FB) followed by CC without feedback (NFB). The second group (NFB FB, n = 40) performed vice versa. Primary endpoints: adequate compression (compression rate between 100 and 120 min −1 and compression depth between 50 and 60 mm); compressions rate within 100–120 min −1 ; compressions depth within 50–60 mm. Results When compared to the performance without feedback, with Mini-VREM feedback compressions were more adequate (FB 35.78 vs. NFB 7.27 , p p p = 0.002). The participants perceived the system to be easy to use with effective feedback. Conclusions The Mini-VREM system was able to improve significantly the CC performance by healthcare professionals and by lay people in a simulated CA scenario, in terms of compression rate and depth. 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 Cardiopulmonary resuscitation (CPR) plays a primary role in first-aid treatment. Instead of the traditional instructor-led training course, we propose a virtual reality system which provides an immersive 3D environment for CPR training with visual and haptic feedback. To simulate a real world CPR experience, our immersive trainer system enables a trainee to perform CPR compressions to a virtual human, inside the virtual world. During the training procedure, the trainee can not only watch his her 3D image performing CPR, but also feel the force feedback from the chest compressions in real-time. To further enhance the visual fidelity, a haptic-enabled deformable model is applied to show the visual change of chest during compression. 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.