Conference proceeding
Identifying Virtual Reality Users Across Domain-Specific Tasks: A Systematic Investigation of Tracked Features for Assembly
2023 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp 396-404
16 Oct 2023
Abstract
Recently, there has been much interest in using virtual reality (VR) tracking data to authenticate or identify users. Most prior research has relied on task-specific characteristics but newer studies have begun investigating task-agnostic, domain-specific approaches. In this paper, we present one of the first systematic investigations of how different combinations of VR tracked devices (i.e., the headset, dominant hand controller, and non-dominant hand controller) and their spatial representations (i.e., position and/or rotation as Euler angles, quaternions, or 6D) affect identification accuracy for domain-specific approaches. We conducted a user study ( n =45) involving participants learning how to assemble two distinct full-scale constructions. Our results indicate that more tracked devices improve identification accuracies for the same assembly task, but only headset features afford the best accuracies across the domain-specific tasks. Our results also indicate that spatial features involving position and any rotation yield better accuracies than either alone.
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Details
- Title
- Identifying Virtual Reality Users Across Domain-Specific Tasks: A Systematic Investigation of Tracked Features for Assembly
- Creators
- Alec G. Moore - University of Central FloridaTiffany D. Do - The University of Texas at DallasNicholas Ruozzi - University of Central FloridaRyan P. McMahan - University of Central Florida
- Publication Details
- 2023 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp 396-404
- Publisher
- IEEE
- Number of pages
- 9
- Grant note
- National Science Foundation (10.13039/100000001)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Web of Science ID
- WOS:001123174400041
- Scopus ID
- 2-s2.0-85180360844
- Other Identifier
- 991021916803604721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Cybernetics
- Computer Science, Interdisciplinary Applications
- Computer Science, Theory & Methods