@inproceedings{96bebadf0cd54de1a9c4c91b6c3ef2d9,
title = "Predicting the torso direction from HMD movements for walk-in-place navigation through deep learning",
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.",
keywords = "Deep learning, Locomotion, Virtual reality, Walking in place",
author = "Juyoung Lee and Andreas Pastor and Hwang, {Jae In} and Kim, {Gerard Jounghyun}",
year = "2019",
month = nov,
day = "12",
doi = "10.1145/3359996.3364709",
language = "English",
series = "Proceedings of the ACM Symposium on Virtual Reality Software and Technology, VRST",
publisher = "Association for Computing Machinery",
editor = "Spencer, {Stephen N.}",
booktitle = "Proceedings - VRST 2019",
note = "25th ACM Symposium on Virtual Reality Software and Technology, VRST 2019 ; Conference date: 12-11-2019 Through 15-11-2019",
}