{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T12:03:23Z","timestamp":1773749003947,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T00:00:00Z","timestamp":1773705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DE210101623"],"award-info":[{"award-number":["DE210101623"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Motion sickness (MS) is a prevalent condition that can significantly degrade user comfort and immersion, particularly in virtual reality (VR) environments. Accurate prediction models are essential for early detection and mitigation of MS symptoms, thereby improving the overall VR experience. Most existing approaches rely on bio-physiological data acquired through body-mounted sensors, which may restrict user mobility and diminish immersion. This study proposes a less intrusive alternative, leveraging head and torso kinematic data for MS prediction. We introduce a hybrid Convolutional\u2013Recurrent Neural Network (C-RNN) designed to capture both spatial and temporal features for enhanced classification accuracy. Using a dataset of 40 participants, the proposed C-RNN outperformed traditional machine learning models\u2014including Support Vector Machines (SVMs), k-Nearest Neighbors (KNN), Decision Trees (DT), and a baseline Recurrent Neural Network (RNN)\u2014across multiple evaluation metrics. The C-RNN achieved 85.63% accuracy, surpassing SVM (60%), KNN (73.75%), DT (74.38%), and RNN (81.88%), with corresponding gains in precision, recall, F1-score, and ROC AUC. These results demonstrate that head\u2013torso motion patterns provide sufficient predictive signal for accurate MS detection, offering a non-intrusive, efficient alternative to physiological sensing that supports improved comfort and sustained immersion in VR.<\/jats:p>","DOI":"10.3390\/computers15030193","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T09:35:57Z","timestamp":1773740157000},"page":"193","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting Cybersickness in Virtual Reality from Head\u2013Torso Kinematics Using a Hybrid Convolutional\u2013Recurrent Network Model"],"prefix":"10.3390","volume":"15","author":[{"given":"Ala","family":"Hag","sequence":"first","affiliation":[{"name":"Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia"}]},{"given":"Houshyar","family":"Asadi","sequence":"additional","affiliation":[{"name":"Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1839-029X","authenticated-orcid":false,"given":"Mohammad Reza Chalak","family":"Qazani","sequence":"additional","affiliation":[{"name":"College of Science and Engineering, James Cook University, Townsville, QLD 4811, Australia"},{"name":"Faculty of Computing and Information Technology (FCIT), Sohar University, Sohar 311, Oman"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7354-260X","authenticated-orcid":false,"given":"Thuong","family":"Hoang","sequence":"additional","affiliation":[{"name":"School of Communication and Creative Arts, Deakin University, Burwood, VIC 3125, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4647-4511","authenticated-orcid":false,"given":"Ambarish","family":"Kulkarni","sequence":"additional","affiliation":[{"name":"School of Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5487-0237","authenticated-orcid":false,"given":"Stefan","family":"Greuter","sequence":"additional","affiliation":[{"name":"School of Information Technology, Deakin University, Geelong, VIC 3216, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0360-5270","authenticated-orcid":false,"given":"Saeid","family":"Nahavandi","sequence":"additional","affiliation":[{"name":"School of Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bertolini, G., and Straumann, D. 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