Thesis Description & Objectives
Cybersickness is a disorder characterized by symptoms such as nausea or discomfort that can occur during or after the use of Virtual Reality (VR) technologies. Similarly to motion sickness, it is mainly caused by inconsistencies between visual and auditory stimuli from the simulated environment and the expected feedback from the vestibular system. Several mitigation and prevention techniques have been proposed and are employed in various commercial products. However, most of them are static, highly situational, or require design choices that negatively affect key aspects of the VR experience such as immersion, naturalness, and sense of presence.
A promising direction that remains underexplored is the use of adaptive mitigation techniques that respond dynamically to the user’s real-time state. Recent advances in physiological monitoring and data-driven modeling make it possible to estimate cybersickness symptoms during VR exposure by analyzing signals such as heart rate or skin conductance, as well as behavioral data like movement patterns and interaction metrics. These indicators, when interpreted using machine learning or rule-based models, offer the opportunity to adapt the virtual environment continuously in response to the user’s current level of discomfort.
The objective of this thesis is to design and implement an adaptive mitigation system capable of modifying VR parameters in real time based on continuous detection of cybersickness indicators. The system will be integrated into an existing VR environment and will be tested by adjusting elements such as motion intensity, visual effects, or field of view in order to alleviate symptoms without significantly compromising immersion. The effectiveness of the approach will be evaluated through a user study, combining subjective and objective measures to assess comfort, presence, and user experience over time.
References:
– Jyotirmay Nag Setu et al., Mazed and Confused: A Dataset of Cybersickness, Working Memory, Mental Load, Physical Load, and Attention During a Real Walking Task in VR, 2024 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 10.1109/ISMAR62088.2024.00121
– Davide Calandra, Fabrizio Lamberti, A Testbed for Studying Cybersickness and its Mitigation in Immersive Virtual Reality, IEEE Transactions on Visualization and Computer Graphics ( Volume: 30, Issue: 12, December 2024), 10.1109/TVCG.2024.3448203
– Rifatul Islam et al., Cybersickness Prediction from Integrated HMD’s Sensors: A Multimodal Deep Fusion Approach using Eye-tracking and Head-tracking Data, 2021 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 10.1109/ISMAR52148.2021.00017
