Machine Learning Methods for the Study of Cybersickness: A Systematic Review

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aut.relation.issue1en_NZ
aut.relation.journalBrain Informen_NZ
aut.relation.startpage24
aut.relation.volume9en_NZ
dc.contributor.authorYang, AHXen_NZ
dc.contributor.authorKasabov, Nen_NZ
dc.contributor.authorCakmak, YOen_NZ
dc.date.accessioned2022-10-24T22:20:27Z
dc.date.available2022-10-24T22:20:27Z
dc.date.copyright2022-10-09en_NZ
dc.date.issued2022-10-09en_NZ
dc.description.abstractThis systematic review offers a world-first critical analysis of machine learning methods and systems, along with future directions for the study of cybersickness induced by virtual reality (VR). VR is becoming increasingly popular and is an important part of current advances in human training, therapies, entertainment, and access to the metaverse. Usage of this technology is limited by cybersickness, a common debilitating condition experienced upon VR immersion. Cybersickness is accompanied by a mix of symptoms including nausea, dizziness, fatigue and oculomotor disturbances. Machine learning can be used to identify cybersickness and is a step towards overcoming these physiological limitations. Practical implementation of this is possible with optimised data collection from wearable devices and appropriate algorithms that incorporate advanced machine learning approaches. The present systematic review focuses on 26 selected studies. These concern machine learning of biometric and neuro-physiological signals obtained from wearable devices for the automatic identification of cybersickness. The methods, data processing and machine learning architecture, as well as suggestions for future exploration on detection and prediction of cybersickness are explored. A wide range of immersion environments, participant activity, features and machine learning architectures were identified. Although models for cybersickness detection have been developed, literature still lacks a model for the prediction of first-instance events. Future research is pointed towards goal-oriented data selection and labelling, as well as the use of brain-inspired spiking neural network models to achieve better accuracy and understanding of complex spatio-temporal brain processes related to cybersickness.en_NZ
dc.identifier.citationBrain Informatics 9, 24 (2022). https://doi.org/10.1186/s40708-022-00172-6
dc.identifier.doi10.1186/s40708-022-00172-6en_NZ
dc.identifier.issn2198-4018en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/15544
dc.languageengen_NZ
dc.publisherSpringer
dc.relation.urihttps://braininformatics.springeropen.com/articles/10.1186/s40708-022-00172-6
dc.rights© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectAIen_NZ
dc.subjectBiometricsen_NZ
dc.subjectCybersicknessen_NZ
dc.subjectDetectionen_NZ
dc.subjectExtended realityen_NZ
dc.subjectMachine learningen_NZ
dc.subjectNeural networksen_NZ
dc.subjectPhysiologicalen_NZ
dc.subjectPredictionen_NZ
dc.subjectReviewen_NZ
dc.subjectSimulatoren_NZ
dc.subjectSystematicen_NZ
dc.subjectVirtual realityen_NZ
dc.titleMachine Learning Methods for the Study of Cybersickness: A Systematic Reviewen_NZ
dc.typeJournal Article
pubs.elements-id481910
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies/School of Engineering, Computer & Mathematical Sciences
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies/ECMS PBRF 2018
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