{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T02:48:03Z","timestamp":1767926883144,"version":"3.49.0"},"reference-count":46,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,4,23]],"date-time":"2023-04-23T00:00:00Z","timestamp":1682208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006769","name":"the Russian Science Foundation","doi-asserted-by":"publisher","award":["22-71-10057"],"award-info":[{"award-number":["22-71-10057"]}],"id":[{"id":"10.13039\/501100006769","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>In virtual reality (VR) systems, a problem is the accurate reproduction of the user\u2019s body in a virtual environment using inverse kinematics because existing motion capture systems have a number of drawbacks, and minimizing the number of key tracking points (KTPs) leads to a large error. To solve this problem, it is proposed to use the concept of a digital shadow and machine learning technologies to optimize the number of KTPs. A technique for movement process data collecting from a virtual avatar is implemented, modeling of nonlinear dynamic processes of human movement based on a digital shadow is carried out, the problem of optimizing the number of KTP is formulated, and an overview of the applied machine learning algorithms and metrics for their evaluation is given. An experiment on a dataset formed from virtual avatar movements shows the following results: three KTPs do not provide sufficient reconstruction accuracy, the choice of five or seven KTPs is optimal; among the algorithms, the most efficient in descending order are AdaBoostRegressor, LinearRegression, and SGDRegressor. During the reconstruction using AdaBoostRegressor, the maximum deviation is not more than 0.25 m, and the average is not more than 0.10 m.<\/jats:p>","DOI":"10.3390\/computation11050085","type":"journal-article","created":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T04:01:23Z","timestamp":1682308883000},"page":"85","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Modeling of Nonlinear Dynamic Processes of Human Movement in Virtual Reality Based on Digital Shadows"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3450-5213","authenticated-orcid":false,"given":"Artem","family":"Obukhov","sequence":"first","affiliation":[{"name":"Laboratory of VR Simulators, Tambov State Technical University, 392000 Tambov, Russia"}]},{"given":"Denis","family":"Dedov","sequence":"additional","affiliation":[{"name":"Laboratory of VR Simulators, Tambov State Technical University, 392000 Tambov, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7109-9114","authenticated-orcid":false,"given":"Andrey","family":"Volkov","sequence":"additional","affiliation":[{"name":"Laboratory of VR Simulators, Tambov State Technical University, 392000 Tambov, Russia"}]},{"given":"Daniil","family":"Teselkin","sequence":"additional","affiliation":[{"name":"Laboratory of VR Simulators, Tambov State Technical University, 392000 Tambov, Russia"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.1007\/s10055-021-00618-y","article-title":"A framework for fidelity evaluation of immersive virtual reality systems","volume":"26","author":"Tanbour","year":"2022","journal-title":"Virtual Real."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"012033","DOI":"10.1088\/1742-6596\/2388\/1\/012033","article-title":"Human motion capture algorithm for creating digital shadows of the movement process","volume":"2388","author":"Obukhov","year":"2022","journal-title":"J. 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