Immersive analysis of user motion in VR applications
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ORIGINAL ARTICLE
Immersive analysis of user motion in VR applications Simon Kloiber1 · Volker Settgast2 · Christoph Schinko2 · Martin Weinzerl3 · Johannes Fritz3 · Tobias Schreck1 · Reinhold Preiner1
© The Author(s) 2020
Abstract With the rise of virtual reality experiences for applications in entertainment, industry, science and medicine, the evaluation of human motion in immersive environments is becoming more important. By analysing the motion of virtual reality users, design choices and training progress in the virtual environment can be understood and improved. Since the motion is captured in a virtual environment, performing the analysis in the same environment provides a valuable context and guidance for the analysis. We have created a visual analysis system that is designed for immersive visualisation and exploration of human motion data. By combining suitable data mining algorithms with immersive visualisation techniques, we facilitate the reasoning and understanding of the underlying motion. We apply and evaluate this novel approach on a relevant VR application domain to identify and interpret motion patterns in a meaningful way. Keywords Virtual reality · Immersive analytics · Movement analysis · Trajectory visualisation · Virtual training evaluation
1 Introduction As consumer-level virtual reality (VR) devices become more powerful, affordable and widely available, VR-based experiences find ever new applications in entertainment, gaming and industry. Nowadays, VR devices provide an integrated high-end tracking of head and bimanual motion off the shelf, without resorting to any additional motion capture gear. These three basic tracking points alone already provide rich information about a user’s motion and actions performed in a VR experience. In fact, the incidental motion data tracked during a VR session is of strong interest for a multitude of VR-based motion analysis tasks, which are relevant for various private, commercial, but also medical and scientific use cases. Examples include the performance analysis and optimisation in task-oriented VR scenarios, but Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00371-020-01942-1) contains supplementary material, which is available to authorized users.
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Simon Kloiber [email protected]
1
Institute of Computer Graphics and Knowledge Visualisation, Graz University of Technology, Graz, Austria
2
Fraunhofer Austria Research GmbH, Graz, Austria
3
AVL List GmbH, Graz, Austria
also the evaluation and assessment in virtual training sessions. The specific key quality of these kinds of use cases is the strong link between rich and complex motion data and the context of the virtual environment it was recorded in. This poses a new challenge for the analysis of human motion recorded in virtual reality. In this paper, we present an immersive approach to analyse such VR motion data. Our key intuition is that such motion data should be analysed in the same perceptive context in which it was recorded, that is
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