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dc.contributor.author황재인-
dc.contributor.author이주영-
dc.contributor.author안드레아스 패스토-
dc.contributor.author김정현-
dc.date.accessioned2021-06-09T04:23:39Z-
dc.date.available2021-06-09T04:23:39Z-
dc.date.issued2019-11-
dc.identifier.issn--
dc.identifier.other54078-
dc.identifier.urihttps://pubs.kist.re.kr/handle/201004/70680-
dc.description.abstractIn 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.-
dc.publisherACM Symposium on Virtual Reality Software and Technology-
dc.subjectvirtual reality-
dc.subjectnavigation-
dc.subjectwalk in place-
dc.subjectdeep learning-
dc.titlePredicting the Torso Direction from HMD Movements for Walk-in-Place Navigation through Deep Learning-
dc.typeConference Paper-
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