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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2402.02725 |
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| _version_ | 1866914692812767232 |
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| author | Salehi, Masoud Javadpour, Nikoo Beisner, Brietta Sanaei, Mohammadamin Gilbert, Stephen B. |
| author_facet | Salehi, Masoud Javadpour, Nikoo Beisner, Brietta Sanaei, Mohammadamin Gilbert, Stephen B. |
| contents | Despite the widespread adoption of Virtual Reality (VR) technology, cybersickness remains a barrier for some users. This research investigates head movement patterns as a novel physiological marker for cybersickness detection. Unlike traditional markers, head movements provide a continuous, non-invasive measure that can be easily captured through the sensors embedded in all commercial VR headsets. We used a publicly available dataset from a VR experiment involving 75 participants and analyzed head movements across six axes. An extensive feature extraction process was then performed on the head movement dataset and its derivatives, including velocity, acceleration, and jerk. Three categories of features were extracted, encompassing statistical, temporal, and spectral features. Subsequently, we employed the Recursive Feature Elimination method to select the most important and effective features. In a series of experiments, we trained a variety of machine learning algorithms. The results demonstrate a 76% accuracy and 83% precision in predicting cybersickness in the subjects based on the head movements. This study contribution to the cybersickness literature lies in offering a preliminary analysis of a new source of data and providing insight into the relationship of head movements and cybersickness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_02725 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cybersickness Detection through Head Movement Patterns: A Promising Approach Salehi, Masoud Javadpour, Nikoo Beisner, Brietta Sanaei, Mohammadamin Gilbert, Stephen B. Machine Learning Signal Processing Despite the widespread adoption of Virtual Reality (VR) technology, cybersickness remains a barrier for some users. This research investigates head movement patterns as a novel physiological marker for cybersickness detection. Unlike traditional markers, head movements provide a continuous, non-invasive measure that can be easily captured through the sensors embedded in all commercial VR headsets. We used a publicly available dataset from a VR experiment involving 75 participants and analyzed head movements across six axes. An extensive feature extraction process was then performed on the head movement dataset and its derivatives, including velocity, acceleration, and jerk. Three categories of features were extracted, encompassing statistical, temporal, and spectral features. Subsequently, we employed the Recursive Feature Elimination method to select the most important and effective features. In a series of experiments, we trained a variety of machine learning algorithms. The results demonstrate a 76% accuracy and 83% precision in predicting cybersickness in the subjects based on the head movements. This study contribution to the cybersickness literature lies in offering a preliminary analysis of a new source of data and providing insight into the relationship of head movements and cybersickness. |
| title | Cybersickness Detection through Head Movement Patterns: A Promising Approach |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2402.02725 |