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15.2018

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  1. 2019-07-17

    The Influence of Autonomous Movement on Adverse Events in Relaxing Virtual Environments Using a Head-Mounted Display

    Background: Virtual reality has been increasingly used to support established psychological interventions, including relaxation techniques. Only limited knowledge about the occurrence and severity of adverse events (AE) (e.g. cybersickness) in relaxing virtual environments is available. The aim of the study was to assess the frequency of AE in virtual environments and factors associated with these. Methods: A sample of 30 healthy participants was included in the study. The participants completed questionnaires on susceptibility of motion sickness, use of and attitudes towards modern technology prior to the exposition to the virtual environment. They then took part in three short virtual scenarios (no movement of the avatar, steady non-autonomous movement, and autonomous movement) using head-mounted displays and rated the occurrence and severity of AE after each scenario. Results: The participants reported high incidence rates of different AEs (40–70%), but only in the scenario with autonomous movement. In the scenarios with no or only limited control over movement approximately 30% reported slight symptoms of dizziness, and 3–7% reported slight nausea. Nevertheless, the occurrence of AEs resulted in reduced relaxation and mood. Gender, age, and usage of computers and gaming consoles had no influence on the incidence or severity of AEs. Discussion: Our results show that virtual reality is a safe technology to be used in clinical psychology, if certain parameters are being minded. Future studies should routinely assess and report AEs in a structured way, to enable more in–depth insights regarding influential factors and potential prevention strategies.

    JVRB, 15(2018), no. 1.

VISIGRAPP 2018
  1. 2019-08-05

    Position Estimation and Calibration of Inertial Motion Capture Systems Using Single Camera

    The paper proposes a hybrid system for position estimation of a motion capture suit and gloves as well as a method for an automatic skeleton calibration for motion capture gloves. The skeleton calibration works with a single image scan of the hand where the skeleton is fitted. The position estimation is based on a synchronization of an inertial motion capture system and a single camera optical setup. The proposed synchronization uses an iterative optimization of an energy potential in image space, minimizing the error between the camera image and a rendered virtual representation of the scene. For each frame, an input skeleton pose from the mocap suit is used to render a silhouette of a subject. Moreover, the local neighborhood around the last known position is searched by matching the silhouette to the distance transform representation of the camera image based on Chamfer matching. Using the combination of the camera tracking and the inertial motion capture suit, it is possible to retrieve the position of the joints that are hidden from the camera view. Using the proposed hybrid technique, it is possible to capture the position even if it cannot be captured by the suit sensors. Our system can be used for both realtime tracking and off-line post-processing of already captured mocap data.

    JVRB, 15(2018), no. 3.

  2. 2019-07-17

    Efficient Error-bounded Curvature Optimization for Smooth Machining Paths

    Automated machining with 3-axis robots requires the generation of tool paths in form of positions of the tool tip. For 5-axis robots, the orientations of the tool at each position needs to be provided, as well. Such a tool path can be described in form of two curves, one for the positional information (as for 3-axis machining) and one for the orientational information, where the orientation is given by the vector that points from a point on the orientation curve to the respective point on the position curve. As the robots need to slow down for sharp turns, i.e., high curvatures in the tool path lead to slow processing, our goal is to generate tool paths with minimized curvatures and a guaranteed error bound. Starting from an initial tool path, which is given in the form of polygonal representations of the position and orientation curves, we generate optimized versions of the curves in the form of B-spline curves that lie within some error bounds of the input path. Our approach first computes an optimized version of the position curve within a tolerance band of the input curve. The outcome of this first step can directly be applied to 3-axis machining. Based on this first step, for 5-axis machining the orientation curve needs to be updated to again fit the position curve. Then, the orientation curve is optimized using a similar approach as for the position curve, but the error bounds are given in the form of tolerance frustums that define the tolerance in lead and tilt. For an efficient optimization procedure, our approach analyzes the input path and splits it into small (partially overlapping) groups before optimizing the position curve. The groups are categorized according to their geometric complexity and handled accordingly using two different optimization procedures. The simpler, but faster algorithm uses a local spline approximation, while the slower, but better algorithm uses a local sleeve approach. These algorithms are adapted to both the position and orientation curve optimization. Subsequently, the groups are combined into a complete tool path in the form of G2- continuous B-spline curves, where we have one such curve for 3-axis machining and two such curves defined over the same knot vector for 5-axis machining.

    JVRB, 15(2018), no. 2.