Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
3-2023
Abstract
Hand redirection is effective so long as the introduced offsets are not noticeably disruptive to users. In this work we investigate the use of physiological and interaction data to detect movement discrepancies between a user's real and virtual hand, pushing towards a novel approach to identify discrepancies which are too large and therefore can be noticed. We ran a study with 22 participants, collecting EEG, ECG, EDA, RSP, and interaction data. Our results suggest that EEG and interaction data can be reliably used to detect visuo-motor discrepancies, whereas ECG and RSP seem to suffer from inconsistencies. Our findings also show that participants quickly adapt to large discrepancies, and that they constantly attempt to establish a stable mental model of their environment. Together, these findings suggest that there is no absolute threshold for possible non-detectable discrepancies; instead, it depends primarily on participants' most recent experience with this kind of interaction.
Keywords
Detection Thresholds, Hand Redirection, Physiological Data, Virtual Reality
Discipline
Graphics and Human Computer Interfaces
Research Areas
Information Systems and Management
Publication
Proceedings of the 2023 IEEE Conference Virtual Reality and 3D User Interfaces (VR), Shanghai, China, March 25-29
First Page
194
Last Page
204
ISBN
9798350348156
Identifier
10.1109/VR55154.2023.00035
Publisher
IEEE
City or Country
Shanghai, China
Citation
FEICK, Martin; REGITZ, Kora; TANG, Anthony; and JUNGBLUTH.
Investigating noticeable hand redirection in virtual reality using physiological and interaction data. (2023). Proceedings of the 2023 IEEE Conference Virtual Reality and 3D User Interfaces (VR), Shanghai, China, March 25-29. 194-204.
Available at: https://ink.library.smu.edu.sg/sis_research/7955
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/VR55154.2023.00035