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.
and present results from their system called RELIVE which is similar to our approach @cite_24 @cite_10 . RELIVE uses data from the Kinect v1 and extract depth and frequency parameters from the motion data with the intention to improve the quality of CPR (training). In contrast to our approach (usage of the integrated Kinect skeleton tracking), they use the raw RGB pixel and depth image data of the Kinect to identify hands, arms, and the training body. The predecessor to RELIVE is probably the Mini-VREM tool, which is a Kinect with software-based audio- and video-feedback-system @cite_27 but requires a marker at the participant's hands (colored bracelet).
@cite_24 @cite_27 @cite_10
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In this paper, we present a novel approach for human action recognition with histograms of 3D joint locations (HOJ3D) as a compact representation of postures. We extract the 3D skelet al joint locations from Kinect depth maps using 's method [6]. The HOJ3D computed from the action depth sequences are reprojected using LDA and then clustered into k posture visual words, which represent the prototypical poses of actions. The temporal evolutions of those visual words are modeled by discrete hidden Markov models (HMMs). In addition, due to the design of our spherical coordinate system and the robust 3D skeleton estimation from Kinect, our method demonstrates significant view invariance on our 3D action dataset. Our dataset is composed of 200 3D sequences of 10 indoor activities performed by 10 individuals in varied views. Our method is real-time and achieves superior results on the challenging 3D action dataset. We also tested our algorithm on the MSR Action 3D dataset and our algorithm outperforms [25] on most of the cases. Since the Microsoft Kinect has been released, the usage of marker-less body pose estimation has been enormously eased. Based on 3D skelet al pose information, complex human gestures and actions can be recognised in real time. However, due to errors in tracking or occlusions, the obtained information can be noisy. Since the RGB-D data is available, the 3D or 2D shape of the person can be used instead. However, depending on the viewpoint and the action to recognise, it might present a low discriminative value. In this paper, the combination of body pose estimation and 2D shape, in order to provide additional characteristic value, is considered so as to improve human action recognition. Using efficient feature extraction techniques, skelet al and silhouette-based features are obtained which are low dimensional and can be obtained in real time. These two features are then combined by means of feature fusion. The proposed approach is validated using a state-of-the-art learning method and the MSR Action3D dataset as benchmark. The obtained results show that the fused feature achieves to improve the recognition rates, outperforming state-of-the-art results in recognition rate and robustness. In this paper we address the problem of modeling and analyzing human motion by focusing on 3D body skeletons. Particularly, our intent is to represent skelet al motion in a geometric and efficient way, leading to an accurate action-recognition system. Here an action is represented by a dynamical system whose observability matrix is characterized as an element of a Grassmann manifold. To formulate our learning algorithm, we propose two distinct ideas: (1) in the first one we perform classification using a Truncated Wrapped Gaussian model, one for each class in its own tangent space. (2) In the second one we propose a novel learning algorithm that uses a vector representation formed by concatenating local coordinates in tangent spaces associated with different classes and training a linear SVM. We evaluate our approaches on three public 3D action datasets: MSR-action 3D, UT-kinect and UCF-kinect datasets; these datasets represent different kinds of challenges and together help provide an exhaustive evaluation. The results show that our approaches either match or exceed state-of-the-art performance reaching 91.21 on MSR-action 3D, 97.91 on UCF-kinect, and 88.5 on UT-kinect. Finally, we evaluate the latency, i.e. the ability to recognize an action before its termination, of our approach and demonstrate improvements relative to other published approaches. HighlightsA human action recognition approach which represents skelet al sequence as point on the Grassmann manifold.A new learning algorithm is introduced for learning human actions.Experiments are performed on three public datasets.Promising success rates are achieved, showing accuracy and better latency performances. 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