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Using Head Tracking Data for Robust Short Term Path Prediction of Human Locomotion

  • Conference paper
Transactions on Computational Science XVIII

Part of the book series: Lecture Notes in Computer Science ((TCOMPUTATSCIE,volume 7848))

Abstract

Modern interactive environments like virtual reality simulators or augmented reality systems often require reliable information about a user’s future intention in order to increase their immersion and usefulness. For many of such systems, where human locomotion is an essential way of interaction, knowing a user’s future walking direction provides relevant information.

This paper explains how head tracking data can be used to retrieve a person’s intended direction of walking. The goal is to provide a reliable and stable path prediction of human locomotion that holds for a few seconds. Using 6 degrees of freedom head tracking data, the head orientation and the head’s movement direction can be derived. Within a user study it is shown that such raw tracking data provides poor prediction results mainly due to noise from gait oscillations. Hence, smoothing filters have to be applied to the data to increase the reliability and robustness of a predictor.

Results of the user study show that double exponential smoothing of a person’s walking direction data in combination with an initialization using the head orientation provides a reliable short term path predictor with high robustness.

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Authors and Affiliations

  1. Innovation Center Virtual Reality (ICVR), Institute of Machine Tools and Manufacturing, ETH Zurich, Switzerland

    Thomas Nescher & Andreas Kunz

Authors
  1. Thomas Nescher
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  2. Andreas Kunz
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Editors and Affiliations

  1. Department of Computer Science, University of Calgary, 2500 University Drive N.W., T2N 1N4, Calgary, AB, Canada

    Marina L. Gavrilova

  2. Exascala Ltd., Unit 9, 97 Rickman Drive, B15 2AL, Birmingham, UK

    C. J. Kenneth Tan

  3. Institut für Graphische Datenverarbeitung, Fraunhofer Gesellschaft, Fraunhoferstraße 5, 64283, Darmstadt, Germany

    Arjan Kuijper

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Nescher, T., Kunz, A. (2013). Using Head Tracking Data for Robust Short Term Path Prediction of Human Locomotion. In: Gavrilova, M.L., Tan, C.J.K., Kuijper, A. (eds) Transactions on Computational Science XVIII. Lecture Notes in Computer Science, vol 7848. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38803-3_10

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  • DOI: https://doi.org/10.1007/978-3-642-38803-3_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38802-6

  • Online ISBN: 978-3-642-38803-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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