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.
RGB-D cameras have already been used in various approaches to support CPR training. for example use Kinect data to model a virtual environment with patient and trainee and drive a haptic device in the real world @cite_19 . In the virtual environment, the performer can see the CPR on a virtual avatar while responding to the sensation of the haptic device. The authors focus more on the basics of cardiac massage than specific optimization of the training.
@cite_19
2031561585 2243794092 2743552164 1972671602
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. The ability to quickly acquire 3D models is an essential capability needed in many disciplines including robotics, computer vision, geodesy, and architecture. In this paper we present a novel method for real-time camera tracking and 3D reconstruction of static indoor environments using an RGB-D sensor. We show that by representing the geometry with a signed distance function (SDF), the camera pose can be efficiently estimated by directly minimizing the error of the depth images on the SDF. As the SDF contains the distances to the surface for each voxel, the pose optimization can be carried out extremely fast. By iteratively estimating the camera poses and integrating the RGB-D data in the voxel grid, a detailed reconstruction of an indoor environment can be achieved. We present reconstructions of several rooms using a hand-held sensor and from onboard an autonomous quadrocopter. Our extensive evaluation on publicly available benchmark data shows that our approach is more accurate and robust than the iterated closest point algorithm (ICP) used by KinectFusion, and yields often a comparable accuracy at much higher speed to feature-based bundle adjustment methods such as RGB-D SLAM for up to medium-sized scenes. Commercial RGB-D cameras provide the possibility of fast, accurate, and cost-effective 3-D scanning solution in a single package. These economical depth cameras provide several advantages over conventional depth sensors, such as sonars and lidars, in specific usage scenarios. In this paper, we analyze the performance of Kinect v2 time-of-flight camera while operating fully submerged underwater in a customized waterproof housing. Camera calibration has been performed for Kinect’s RGB and NIR cameras, and the effect of calibration on the generated 3-D mesh is discussed in detail. To overcome the effect of refraction of light due to the sensor housing and water, we propose a time-of-flight correction method and a fast, accurate and intuitive refraction correction method that can be applied to the acquired depth images, during 3-D mesh generation. Experimental results show that the Kinect v2 can acquire point cloud data up to 650 mm. The reconstruction results have been analyzed qualitatively and quantitatively, and confirm that the 3-D reconstruction of submerged objects at small distances is possible without the requirement of any external NIR light source. The proposed algorithms successfully generated 3-D mesh with a mean error of ±6 mm at a frame rate of nearly 10 fps. We acquired a large data set of RGB, IR and depth data from a submerged Kinect v2. The data set covers a large variety of objects scanned underwater and is publicly available for further use, along with the Kinect waterproof housing design and correction filter codes. The research is aimed toward small-scale research activities and economical solution for 3-D scanning underwater. Applications such as coral reef mapping and underwater SLAM in shallow waters for ROV’s can be a viable application area that can benefit from results achieved. RGB-D cameras (such as the Microsoft Kinect) are novel sensing systems that capture RGB images along with per-pixel depth information. In this paper we investigate how such cameras can be used for building dense 3D maps of indoor environments. Such maps have applications in robot navigation, manipulation, semantic mapping, and telepresence. We present RGB-D Mapping, a full 3D mapping system that utilizes a novel joint optimization algorithm combining visual features and shape-based alignment. Visual and depth information are also combined for view-based loop-closure detection, followed by pose optimization to achieve globally consistent maps. We evaluate RGB-D Mapping on two large indoor environments, and show that it effectively combines the visual and shape information available from RGB-D cameras.