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So Predictable! Continuous 3D Hand Trajectory Prediction in Virtual Reality

Authors:

Nisal Menuka Gamage,

Deepana Ishtaweera,

Anusha WithanaAuthors Info & Claims

UIST '21: The 34th Annual ACM Symposium on User Interface Software and Technology

Pages 332 - 343

https://doi.org/10.1145/3472749.3474753

Published: 12 October 2021 Publication History

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Abstract

We contribute a novel user- and activity-independent kinematics-based regressive model for continuously predicting ballistic hand movements in virtual reality (VR). Compared to prior work on end-point prediction, continuous hand trajectory prediction in VR enables an early estimation of future events such as collisions between the user’s hand and virtual objects such as UI widgets. We developed and validated our prediction model through a user study with 20 participants. The study collected hand motion data with a 3D pointing task and a gaming task with three popular VR games. Results show that our model can achieve a low Root Mean Square Error (RMSE) of 0.80 cm, 0.85 cm and 3.15 cm from future hand positions ahead of 100 ms, 200 ms and 300 ms respectively across all the users and activities. In pointing tasks, our predictive model achieves an average angular error of 4.0° and 1.5° from the true landing position when 50% and 70% of the way through the movement. A follow-up study showed that the model can be applied to new users and new activities without further training.

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Cited By

Martinez EMalik AMcMahan R(2024)CLOVR: Collecting and Logging OpenVR Data from SteamVR Applications2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)10.1109/VRW62533.2024.00095(485-492)Online publication date: 16-Mar-2024
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cover image ACM Conferences

UIST '21: The 34th Annual ACM Symposium on User Interface Software and Technology

October 2021

1357 pages

ISBN:9781450386357

DOI:10.1145/3472749

Editors:
Jeffrey Nichols
Apple, USA
,
Ranjitha Kumar
UIUC, USA
,
Michael Nebeling
University of Michigan, USA

Copyright © 2021 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 12 October 2021

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UIST '21: The 34th Annual ACM Symposium on User Interface Software and Technology

October 10 - 14, 2021

Virtual Event, USA

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Overall Acceptance Rate 842 of 3,967 submissions, 21%

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Cited By

Martinez EMalik AMcMahan R(2024)CLOVR: Collecting and Logging OpenVR Data from SteamVR Applications2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)10.1109/VRW62533.2024.00095(485-492)Online publication date: 16-Mar-2024
https://doi.org/10.1109/VRW62533.2024.00095
Mitra MKumar GChakrabarti PBiswas P(2024)Enhanced Human-Robot Collaboration with Intent Prediction using Deep Inverse Reinforcement Learning2024 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA57147.2024.10610595(7880-7887)Online publication date: 13-May-2024
https://doi.org/10.1109/ICRA57147.2024.10610595
Kahanowich NSintov A(2024)Learning Human-Arm Reaching Motion Using a Wearable Device in Human–Robot CollaborationIEEE Access10.1109/ACCESS.2024.336566112(24855-24865)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3365661
Chung CLee S(2024)Continuous Prediction of Pointing Targets With Motion and Eye-Tracking in Virtual RealityIEEE Access10.1109/ACCESS.2024.335078812(5933-5946)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3350788
Mayor JCalleja PFuentes-Hurtado F(2024)Long short-term memory prediction of user’s locomotion in virtual realityVirtual Reality10.1007/s10055-024-00962-928:1Online publication date: 5-Mar-2024
https://dl.acm.org/doi/10.1007/s10055-024-00962-9
Shao QWang JZhou BTran VKrishnan GNayar S(2023)N-euro PredictorProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36108847:3(1-25)Online publication date: 27-Sep-2023
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Yi XWang XLi JLi H(2023)Examining the Fine Motor Control Ability of Linear Hand Movement in Virtual Reality2023 IEEE Conference Virtual Reality and 3D User Interfaces (VR)10.1109/VR55154.2023.00058(427-437)Online publication date: Mar-2023
https://doi.org/10.1109/VR55154.2023.00058
Lee SAsbeck A(2023)A Deep Learning-Based Approach for Foot Placement PredictionIEEE Robotics and Automation Letters10.1109/LRA.2023.32905218:8(4959-4966)Online publication date: Aug-2023
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Bao WChen LZeng LLi ZXu YYuan JKong Y(2023)Uncertainty-aware State Space Transformer for Egocentric 3D Hand Trajectory Forecasting2023 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV51070.2023.01260(13656-13665)Online publication date: 1-Oct-2023
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Usuba HYamanaka SSato JMiyashita H(2022)Predicting Touch Accuracy for Rectangular Targets by Using One-Dimensional Task ResultsProceedings of the ACM on Human-Computer Interaction10.1145/35677326:ISS(525-537)Online publication date: 14-Nov-2022
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