Machine learning methods for the study of cybersickness: a systematic review
Abstract This 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 train...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2022-10-01
|
| Series: | Brain Informatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40708-022-00172-6 |
| _version_ | 1798030051076210688 |
|---|---|
| author | Alexander Hui Xiang Yang Nikola Kasabov Yusuf Ozgur Cakmak |
| author_facet | Alexander Hui Xiang Yang Nikola Kasabov Yusuf Ozgur Cakmak |
| author_sort | Alexander Hui Xiang Yang |
| collection | DOAJ |
| description | Abstract This 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. |
| first_indexed | 2024-04-11T19:34:04Z |
| format | Article |
| id | doaj.art-518bfc38e16c41b3874c63a29c9ed2d8 |
| institution | Directory Open Access Journal |
| issn | 2198-4018 2198-4026 |
| language | English |
| last_indexed | 2024-04-11T19:34:04Z |
| publishDate | 2022-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Brain Informatics |
| spelling | doaj.art-518bfc38e16c41b3874c63a29c9ed2d82022-12-22T04:06:55ZengSpringerOpenBrain Informatics2198-40182198-40262022-10-019112510.1186/s40708-022-00172-6Machine learning methods for the study of cybersickness: a systematic reviewAlexander Hui Xiang Yang0Nikola Kasabov1Yusuf Ozgur Cakmak2Department of Anatomy, University of OtagoKEDRI, School of Engineering, Computer and Mathematical Sciences, Auckland University of TechnologyDepartment of Anatomy, University of OtagoAbstract This 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.https://doi.org/10.1186/s40708-022-00172-6CybersicknessDetectionPredictionBiometricsPhysiologicalReview |
| spellingShingle | Alexander Hui Xiang Yang Nikola Kasabov Yusuf Ozgur Cakmak Machine learning methods for the study of cybersickness: a systematic review Brain Informatics Cybersickness Detection Prediction Biometrics Physiological Review |
| title | Machine learning methods for the study of cybersickness: a systematic review |
| title_full | Machine learning methods for the study of cybersickness: a systematic review |
| title_fullStr | Machine learning methods for the study of cybersickness: a systematic review |
| title_full_unstemmed | Machine learning methods for the study of cybersickness: a systematic review |
| title_short | Machine learning methods for the study of cybersickness: a systematic review |
| title_sort | machine learning methods for the study of cybersickness a systematic review |
| topic | Cybersickness Detection Prediction Biometrics Physiological Review |
| url | https://doi.org/10.1186/s40708-022-00172-6 |
| work_keys_str_mv | AT alexanderhuixiangyang machinelearningmethodsforthestudyofcybersicknessasystematicreview AT nikolakasabov machinelearningmethodsforthestudyofcybersicknessasystematicreview AT yusufozgurcakmak machinelearningmethodsforthestudyofcybersicknessasystematicreview |