Nowadays, there is still a challenge in virtual reality to obtain an accurate displacement prediction of the user. This could be a future key element to apply in the so-called redirected walking methods. Meanwhile, deep learning provides us with new tools to reach greater achievements in this type of prediction. Specifically, long short-term memory recurrent neural networks obtained promising results recently. This gives us clues to continue researching in this line to predict virtual reality user’s displacement. This manuscript focuses on the collection of positional data and a subsequent new way to train a deep learning model to obtain more accurate predictions. The data were collected with 44 participants and it has been analyzed with different existing prediction algorithms. The best results were obtained with a new idea, the use of rotation quaternions and the three dimensions to train the previously existing models. The authors strongly believe that there is still much room for improvement in this research area by means of the usage of new deep learning models.
https://link.springer.com/article/10.1007/s10055-024-00962-9
About Jesús Mayor
Jesús Mayor is since 2019 a full-time lecturer and researcher in Politécnica de Madrid University. He received MS degree in Computer Science (CEU San Pablo, 2013), MS degree in Computer Graphics (U-tad, 2014) and PhD degree in Computer Science (Rey Juan Carlos University, 2020) in Madrid. His studies are focused on computer graphics and data science
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