Predicting the Torso Direction from HMD Movements for Walk-in-Place Navigation through Deep Learning
Ju Young Lee, Andréas Pastor, Jae‐In Hwang, Gerard Jounghyun Kim · 2019
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.