Lee, Juyoung Pastor, Andreas Hwang, Jae-In Kim, Gerard Jounghyun 2024-01-19T10:37:31Z 2024-01-19T10:37:31Z 2022-03-07 2019-11 https://pubs.kist.re.kr/handle/201004/114316 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. English ASSOC COMPUTING MACHINERY Predicting the Torso Direction from HMD Movements for Walk-in-Place Navigation through Deep Learning Conference 10.1145/3359996.3364709 1 25th ACM Symposium on Virtual Reality Software and Technology 25th ACM Symposium on Virtual Reality Software and Technology US Western Sydney Univ, Sydney, AUSTRALIA 2019-11-12 25TH ACM SYMPOSIUM ON VIRTUAL REALITY SOFTWARE AND TECHNOLOGY (VRST 2019) 000527364800083 2-s2.0-85076160498