Investigating Personalization Techniques for Improved Cybersickness Prediction in Virtual Reality Environments
In recent cybersickness research, there has been a growing interest in predicting cybersickness using real-time physiological data such as heart rate, galvanic skin response, eye tracking, postural sway, and electroencephalogram. However, the impact of individual factors such as age and gender, whic...
| Veröffentlicht in: | IEEE transactions on visualization and computer graphics. - 1996. - 30(2024), 5 vom: 16. Mai, Seite 2368-2378 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2024
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on visualization and computer graphics |
| Schlagworte: | Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. |
| Zusammenfassung: | In recent cybersickness research, there has been a growing interest in predicting cybersickness using real-time physiological data such as heart rate, galvanic skin response, eye tracking, postural sway, and electroencephalogram. However, the impact of individual factors such as age and gender, which are pivotal in determining cybersickness susceptibility, remains unknown in predictive models. Our research seeks to address this gap, underscoring the necessity for a more personalized approach to cybersickness prediction to ensure a better, more inclusive virtual reality experience. We hypothesize that a personalized cybersickness prediction model would outperform non-personalized models in predicting cybersickness. Evaluating this, we explored four personalization techniques: 1) data grouping, 2) transfer learning, 3) early shaping, and 4) sample weighing using an open-source cybersickness dataset. Our empirical results indicate that personalized models significantly improve prediction accuracy. For instance, with early shaping, the Deep Temporal Convolutional Neural Network (DeepTCN) model achieved a 69.7% reduction in RMSE compared to its non-personalized version. Our study provides evidence of personalization techniques' benefits in improving cybersickness prediction. These findings have implications for developing personalized cybersickness prediction models tailored to individual differences, which can be used to develop personalized cybersickness reduction techniques in the future |
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| Beschreibung: | Date Completed 23.04.2024 Date Revised 03.05.2024 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1941-0506 |
| DOI: | 10.1109/TVCG.2024.3372122 |