Impact of HMDs on Spatial Navigation (Meta)
This project explored the impact of using head mounted displays (HMDs) on typical real world movement in a variety of settings. Currently, there are limited insights into the relative degree to which using HMDs, and the attentional demands associated with their use, contribute to perturbations in human movement, and affect gait. We measured gait while people wore a variety of HMDs and walking around a path with different properties.
Publications
Balachandran, B. K., Yeung, T., Billinghurst, M., Stamenkovic, A., Mahnan, A., & Lukosch, S. (2024, October). Navigating Virtual Realms: The Impact of Wearing HMDs on Human Gait. In 2024 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) (pp. 425-426). IEEE.
VR.net: A dataset for VMS research (Meta)
VR.net is a motion sickness dataset comprising 165-hour gameplay videos from 100 real-world games spanning ten diverse genres, evaluated by 500 participants. VR.net accurately assigns 24 motion sickness-related labels for each video frame, such as camera/object movement, depth of field, and motion flow. To do this we created a tool to automatically and extract ground truth data from 3D engines’ rendering pipelines without accessing the VR games’ source code. The dataset is freely available and useful for researchers conducting motion sickness research in VR.
Publications
Wen, E., Gupta, C., Sasikumar, P., Billinghurst, M., Wilmott, J., Skow, E., … & Nanayakkara, S. (2024). Vr. net: A real-world dataset for virtual reality motion sickness research. IEEE Transactions on Visualization and Computer Graphics, 30(5), 2330-2336.
Estimating simulator sickness with ML (Meta)
This project explored novel ways to estimate simulator sickness in HMDs using machine learning (ML) and 3D motion data, informed by user-labeled simulator sickness data and user analysis. A VR user study was conducted, which decomposed motion data and used an instant dial-based sickness scoring mechanism. We were able to emulate typical VR usage and collect user simulator sickness scores. Using the results from this we developed a novel deep learning-based solution to better estimate simulator sickness with decomposed 3D motion features and user profile information.
Publications
Zhao, J., Tran, K. T., Chalmers, A., Hoh, W. K., Yao, R., Dey, A., … & Rhee, T. (2023, October). Deep learning-based simulator sickness estimation from 3d motion. In 2023 IEEE International Symposium on Mixed and Augmented Reality (ISMAR) (pp. 39-48). IEEE.
Chalmers, A., Zhao, J., Khuan Hoh, W., Drown, J., Finnie, S., Yao, R., … & Rhee, T. (2023). A Motion-Simulation Platform to Generate Synthetic Motion Data for Computer Vision Tasks. In SIGGRAPH Asia 2023 Technical Communications (pp. 1-
Uncovering Signs of VIMS via Attention (Meta)
This study addresses a critical challenge in VR: the detection of visually induced motion sickness (VIMS). By pioneering the integration of head, eye, and mouth movement data, we developed a novel multi-modalities based approach for early detection of VIMS. Our Attention-VR model is designed to dynamically assign weights to the more important modalities among the three (head, eye, and mouth) for identifying the early signs of VIMS. To create the model, we collected multi-modal data from 38 participants experiencing a VR rollercoaster environment, then we validated the model using data from commercial VR games.
Publications
Chang, E., Lim, E., Cho, H., Hart, J., Dey, A., Zhang, Z., Billinghurst, M. (2025, October). Uncovering Signs of Visually Induced Motion Sickness via Attention-VR. In 2025 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). IEEE.
