Rifatul Islam
Orcid: 0000-0002-4305-9964
According to our database1,
Rifatul Islam
authored at least 12 papers
between 2020 and 2024.
Collaborative distances:
Collaborative distances:
Timeline
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
On csauthors.net:
Bibliography
2024
Investigating Personalization Techniques for Improved Cybersickness Prediction in Virtual Reality Environments.
IEEE Trans. Vis. Comput. Graph., May, 2024
Towards Optimized Cybersickness Prediction for Computationally Constrained Standalone Virtual Reality Devices.
Proceedings of the IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, 2024
2023
Proceedings of the IEEE Conference Virtual Reality and 3D User Interfaces, 2023
2022
Proceedings of the IEEE International Symposium on Mixed and Augmented Reality, 2022
Towards Forecasting the Onset of Cybersickness by Fusing Physiological, Head-tracking and Eye-tracking with Multimodal Deep Fusion Network.
Proceedings of the IEEE International Symposium on Mixed and Augmented Reality, 2022
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022
2021
VR Sickness Prediction from Integrated HMD's Sensors using Multimodal Deep Fusion Network.
CoRR, 2021
CyberSense: A Closed-Loop Framework to Detect Cybersickness Severity and Adaptively apply Reduction Techniques.
Proceedings of the IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, 2021
Cybersickness Prediction from Integrated HMD's Sensors: A Multimodal Deep Fusion Approach using Eye-tracking and Head-tracking Data.
Proceedings of the IEEE International Symposium on Mixed and Augmented Reality, 2021
2020
Automatic Detection of Cybersickness from Physiological Signal in a Virtual Roller Coaster Simulation.
Proceedings of the 2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, 2020
Proceedings of the 2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, 2020
Automatic Detection and Prediction of Cybersickness Severity using Deep Neural Networks from user's Physiological Signals.
Proceedings of the 2020 IEEE International Symposium on Mixed and Augmented Reality, 2020