A cybersickness review: causes, strategies, and classification methods
Virtual reality (VR) and head-mounted displays are continually gaining popularity in various fields such as education, military, entertainment, and health. Although such technologies provide a high sense of immersion, they can also trigger symptoms of discomfort. This condition is called cybersick...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Brazilian Computer Society
2021-11-01
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| Series: | Journal on Interactive Systems |
| Subjects: | |
| Online Access: | https://sol.sbc.org.br/journals/index.php/jis/article/view/2058 |
| _version_ | 1811167808087654400 |
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| author | Thiago Porcino Daniela Trevisan Esteban Clua |
| author_facet | Thiago Porcino Daniela Trevisan Esteban Clua |
| author_sort | Thiago Porcino |
| collection | DOAJ |
| description |
Virtual reality (VR) and head-mounted displays are continually gaining popularity in various fields such as education, military, entertainment, and health. Although such technologies provide a high sense of immersion, they can also trigger symptoms of discomfort. This condition is called cybersickness (CS) and is quite popular in recent virtual reality research. In this work we first present a review of the literature on theories of discomfort manifestations usually attributed to virtual reality environments. Following, we reviewed existing strategies aimed at minimizing CS problems and discussed how the CS measurement has been conducted based on subjective, biosignal (or objective), and users profile data. We also describe and discuss related works that are aiming to mitigate cybersickness problems using deep and symbolic machine learning approaches. Although some works used methods to make deep learning explainable, they are not strongly affirmed by literature. For this reason in this work we argue that symbolic classifiers can be a good way to identify CS causes, once they possibilities human-readability which is crucial for analyze the machine learning decision paths. In summary, from a total of 157 observed studies, 24 were excluded. Moreover, we believe that this work facilitates researchers to identify the leading causes for most discomfort situations in virtual reality environments, associate the most recommended strategies to minimize such discomfort, and explore different ways to conduct experiments involving machine learning to overcome cybersickness.
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| first_indexed | 2024-04-10T16:15:16Z |
| format | Article |
| id | doaj.art-c7a5db0705534e8dbcca07643afc9569 |
| institution | Directory Open Access Journal |
| issn | 2763-7719 |
| language | English |
| last_indexed | 2024-04-10T16:15:16Z |
| publishDate | 2021-11-01 |
| publisher | Brazilian Computer Society |
| record_format | Article |
| series | Journal on Interactive Systems |
| spelling | doaj.art-c7a5db0705534e8dbcca07643afc95692023-02-09T18:06:58ZengBrazilian Computer SocietyJournal on Interactive Systems2763-77192021-11-0112110.5753/jis.2021.2058A cybersickness review: causes, strategies, and classification methodsThiago Porcino0Daniela Trevisan1Esteban Clua2Universidade Federal FluminenseUniversidade Federal FluminenseUniversidade Federal Fluminense Virtual reality (VR) and head-mounted displays are continually gaining popularity in various fields such as education, military, entertainment, and health. Although such technologies provide a high sense of immersion, they can also trigger symptoms of discomfort. This condition is called cybersickness (CS) and is quite popular in recent virtual reality research. In this work we first present a review of the literature on theories of discomfort manifestations usually attributed to virtual reality environments. Following, we reviewed existing strategies aimed at minimizing CS problems and discussed how the CS measurement has been conducted based on subjective, biosignal (or objective), and users profile data. We also describe and discuss related works that are aiming to mitigate cybersickness problems using deep and symbolic machine learning approaches. Although some works used methods to make deep learning explainable, they are not strongly affirmed by literature. For this reason in this work we argue that symbolic classifiers can be a good way to identify CS causes, once they possibilities human-readability which is crucial for analyze the machine learning decision paths. In summary, from a total of 157 observed studies, 24 were excluded. Moreover, we believe that this work facilitates researchers to identify the leading causes for most discomfort situations in virtual reality environments, associate the most recommended strategies to minimize such discomfort, and explore different ways to conduct experiments involving machine learning to overcome cybersickness. https://sol.sbc.org.br/journals/index.php/jis/article/view/2058virtual realityhead-mounted displayscybersicknesscausesstrategiesmachine learning |
| spellingShingle | Thiago Porcino Daniela Trevisan Esteban Clua A cybersickness review: causes, strategies, and classification methods Journal on Interactive Systems virtual reality head-mounted displays cybersickness causes strategies machine learning |
| title | A cybersickness review: causes, strategies, and classification methods |
| title_full | A cybersickness review: causes, strategies, and classification methods |
| title_fullStr | A cybersickness review: causes, strategies, and classification methods |
| title_full_unstemmed | A cybersickness review: causes, strategies, and classification methods |
| title_short | A cybersickness review: causes, strategies, and classification methods |
| title_sort | cybersickness review causes strategies and classification methods |
| topic | virtual reality head-mounted displays cybersickness causes strategies machine learning |
| url | https://sol.sbc.org.br/journals/index.php/jis/article/view/2058 |
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