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 Introduction Relive is a serious game focusing on increasing kids and young adults’ awareness on CPR. We evaluated the use of Relive on schoolchildren. Methods A longitudinal, prospective study was carried out in two high schools in Italy over a 8-month period, divided in three phases: baseline, competition, and retention. Improvement in schoolchildren’s CPR awareness, in terms of knowledge (MCQ results) and skills (chest compression (CC) rate and depth), was evaluated. Usability of Relive and differences in CC performance according to sex and BMI class were also evaluated. Results At baseline, students performed CC with a mean depth of 31 mm and a rate of 95 cpm. In the competition phase, students performed CC with a mean depth of 46 mm and a rate of 111 cpm. In the retention phase, students performed CC with a mean depth of 47 mm and a rate of 131 cpm. Thus, the training session with Relive during the competition phase affected positively both CC depth (p Conclusions Relive is an useful tool to spread CPR knowledge and improve CPR skills in schoolchildren. 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. Cardiopulmonary resuscitation (CPR) is a first-aid key survival technique used to stimulate breathing and keep blood flowing to the heart. Its effective administration can significantly increase the chances of survival in victims of cardiac arrest. In this paper, we propose a markerless system for quality CPR training based on RGB-D (RGB + Depth) sensors, called RELIVE . Then, we report the results of a series of experimental tests conducted to evaluate RELIVE tracking performance. The proposed system is able to accurately track the 3-D position of the hands performing CPR by means of RGB-D sensors to estimate the chest compression rate and depth, providing a real-time visual audio feedback about the rescuer's performance. Finally, the system usability has been assessed by both healthcare professionals and lay people.