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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\u2019s 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.<\/jats:p>","DOI":"10.1007\/s10055-024-00962-9","type":"journal-article","created":{"date-parts":[[2024,3,5]],"date-time":"2024-03-05T04:02:38Z","timestamp":1709611358000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Long short-term memory prediction of user\u2019s locomotion in virtual reality"],"prefix":"10.1007","volume":"28","author":[{"given":"Jesus","family":"Mayor","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pablo","family":"Calleja","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Felix","family":"Fuentes-Hurtado","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,5]]},"reference":[{"key":"962_CR1","doi-asserted-by":"publisher","unstructured":"Azmandian M, Grechkin T, Bolas M, Suma E (2016) The redirected walking toolkit: a unified development platform for exploring large virtual environments. 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No extraneous procedures or experimental devices have been used that could cause harm. According to current legislation, all subjects gave their consent to participate in the experiment and to treat the data anonymously, in no case being able to be identified personally.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"This research does not have a conflict of interest. Funding does not affect the nature of the research. The nature of the research also does not allow for a personal interest that could affect the experiments. All experiments are reproducible, and the data are objective.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"65"}}