Predicting the torso direction from HMD movements for walk-in-place navigation through deep learning

Juyoung Lee, Andreas Pastor, Jae In Hwang, Gerard Jounghyun Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose to use the deep learning technique to estimate and predict the torso direction from the head movements alone. The prediction allows to implement the walk-in-place navigation interface without additional sensing of the torso direction, and thereby improves the convenience and usability. We created a small dataset and tested our idea by training an LSTM model and obtained a 3-class prediction rate of about 90%, a figure higher than using other conventional machine learning techniques. While preliminary, the results show the possible inter-dependence between the viewing and torso directions, and with richer dataset and more parameters, a more accurate level of prediction seems possible.

Original languageEnglish
Title of host publicationProceedings - VRST 2019
Subtitle of host publication25th ACM Symposium on Virtual Reality Software and Technology
EditorsStephen N. Spencer
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450370011
DOIs
Publication statusPublished - 2019 Nov 12
Event25th ACM Symposium on Virtual Reality Software and Technology, VRST 2019 - Sydney, Australia
Duration: 2019 Nov 122019 Nov 15

Publication series

NameProceedings of the ACM Symposium on Virtual Reality Software and Technology, VRST

Conference

Conference25th ACM Symposium on Virtual Reality Software and Technology, VRST 2019
CountryAustralia
CitySydney
Period19/11/1219/11/15

Keywords

  • Deep learning
  • Locomotion
  • Virtual reality
  • Walking in place

ASJC Scopus subject areas

  • Software

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  • Cite this

    Lee, J., Pastor, A., Hwang, J. I., & Kim, G. J. (2019). Predicting the torso direction from HMD movements for walk-in-place navigation through deep learning. In S. N. Spencer (Ed.), Proceedings - VRST 2019: 25th ACM Symposium on Virtual Reality Software and Technology [3364709] (Proceedings of the ACM Symposium on Virtual Reality Software and Technology, VRST). Association for Computing Machinery. https://doi.org/10.1145/3359996.3364709