Cybersickness Detection

Cybersickness detection research aims to identify and predict motion sickness experienced in virtual reality (VR) environments, improving user experience and VR application development. Current research focuses on leveraging diverse data sources, including head movements, eye tracking, physiological signals (e.g., heart rate, galvanic skin response), and user-reported symptoms, employing machine learning algorithms like boosting machines, decision trees, and recurrent neural networks to build predictive models. These efforts emphasize explainable AI techniques to enhance model transparency and facilitate the development of more effective cybersickness mitigation strategies, ultimately leading to safer and more enjoyable VR experiences.