Robustness and static-positional accuracy of the SteamVR 1.0 virtual reality tracking system

The use of low-cost immersive virtual reality systems is rapidly expanding. Several studies started to analyse the accuracy of virtual reality tracking systems, but they did not consider in depth the effects of external interferences in the working area. In line with that, this study aimed at exploring the static-positional accuracy and the robustness to occlusions inside the capture volume of the SteamVR (1.0) tracking system. To do so, we ran 3 different tests in which we acquired the position of HTC Vive PRO Trackers (2018 version) on specific points of a grid drawn on the floor, in regular tracking conditions and with partial and total occlusions. The tracking system showed a high inter- and intra-rater reliability and detected a tilted surface with respect to the floor plane. Every acquisition was characterised by an initial random offset. We estimated an average accuracy of 0.5 ± 0.2 cm across the entire grid (XY-plane), noticing that the central points were more accurate (0.4 ± 0.1 cm) than the outer ones (0.6 ± 0.1 cm). For the Z-axis, the measurements showed greater variability and the accuracy was equal to 1.7 ± 1.2 cm. Occlusion response was tested using nonparametric Bland–Altman statistics, which highlighted the robustness of the tracking system. In conclusion, our results promote the SteamVR system for static measures in the clinical field. The computed error can be considered clinically irrelevant for exercises aimed at the rehabilitation of functional movements, whose several motor outcomes are generally measured on the scale of metres.

Fast automatic camera network calibration through human mesh recovery

Camera calibration is a necessary preliminary step in computer vision for the estimation of the position of objects in the 3D world. Despite the intrinsic camera parameters can be easily computed offline, extrinsic parameters need to be computed each time a camera changes its position, thus not allowing for fast and dynamic network re-configuration. In this paper we present an unsupervised and automatic framework for the estimation of the extrinsic parameters of a camera network, which leverages on optimised 3D human mesh recovery from a single image, and which does not require the use of additional markers. We show how it is possible to retrieve the real-world position of the cameras in the network together with the floor plane, exploiting regular RGB images and with a weak prior knowledge of the internal parameters. Our framework can also work with a single camera and in real-time, allowing the user to add, re-position, or remove cameras from the network in a dynamic fashion.

Sphinx Modified Position For Scaphocephalic And Complex Craniofacial Patients. Technical Note

Background The development of less invasive surgical techniques means that surgical remodelling techniques can be performed at younger ages in more complex situations and at lower cost. Methods Descriptive study Results We describe the technique of positioning and reviews our experience with the partially-modified sphinx position (MSP) in a serie of 234 patients, 96 of these endoscopic procedures (EP). Patient position on the operating table with regard to the treatment area, as well as the position of the treatment area in regard to the floor plane, is paramount for their anaesthetic and surgical management. Conclusions We consider that MSP is safe, facilitates access to the operative field, shortens surgery time, reduces bleeding and offers maximum airway and cranio cervical safety in patients undergoing surgery to correct cranial vault pathologies.

Robot Navigation Control Based on Monocular Images: An Image Processing Algorithm for Obstacle Avoidance Decisions

This paper covers the use of monocular vision to control autonomous navigation for a robot in a dynamically changing environment. The solution focused on using colour segmentation against a selected floor plane to distinctly separate obstacles from traversable space: this is then supplemented with canny edge detection to separate similarly coloured boundaries to the floor plane. The resulting binary map (where white identifies an obstacle-free area and black identifies an obstacle) could then be processed by fuzzy logic or neural networks to control the robot’s next movements. Findings show that the algorithm performed strongly on solid coloured carpets, wooden, and concrete floors but had difficulty in separating colours in multicoloured floor types such as patterned carpets.

Spatial Reference in Multiple Object Tracking

Spatial reference in multiple object tracking is available from configurations of dynamic objects and static reference objects. In three experiments, we studied the use of spatial reference in tracking and in relocating targets after abrupt scene rotations. Observers tracked 1, 2, 3, 4, and 6 targets in 3D scenes, in which white balls moved on a square floor plane. The floor plane was either visible thus providing static spatial reference or it was invisible. Without scene rotations, the configuration of dynamic objects provided sufficient spatial reference and static spatial reference was not advantageous. In contrast, with abrupt scene rotations of 20°, static spatial reference supported in relocating targets. A wireframe floor plane lacking local visual detail was as effective as a checkerboard. Individually colored geometric forms as static reference objects provided no additional benefit either, even if targets were centered on these forms at the abrupt scene rotation. Individualizing the dynamic objects themselves by color for a brief interval around the abrupt scene rotation, however, did improve performance. We conclude that attentional tracking of moving targets proceeds within dynamic configurations but detached from static local background.