The DOI matches its Crossref record, with a small difference in title or year; 1 of the 1 references it cites were found in a registry or an index.
Evaluation of a commodity VR interaction device for gestural object manipulation in a three dimensional work environment
Abstract
Designers and engineers working in the computer-aided drafting (CAD) and computer-aided engineering (CAE) domains routinely interact with specialized computer software featuring three dimensional (3D) work environments. These professionals must manipulate virtual objects or components within this 3D work environment, but typically use traditional interaction devices with outdated technology that are more suitable for 2D work tasks. Current CAD and CAE software is designed to accommodate outdated interaction technology, but this functionality comes at the cost of efficiency in the virtual workspace. A new class of affordable interaction devices with characteristics and specifications of high-end virtual reality interaction devices is now available to consumers. These commodity VR interaction devices monitor the position and orientation of a user's hands through space to control aspects of desktop software in ways that are impossible with the traditional mouse and keyboard pair. They can be integrated with CAD or CAE software to allow gestural control of objects throughout a 3D work environment.\nTo evaluate the feasibility of gestural control for 3D work environments, a commercially available commodity VR interaction device was selected and integrated with specific 3D software. Gestures to control aspects of the software are developed and organized into a taxonomy. Select gestures are integrated with the software and evaluated against traditional interaction methods, using the Natural Goals Operators Methods Selection Rules Language (NGOMSL) concept. The evaluation results show that gestural interaction is efficient for object manipulation tasks, but a traditional keyboard or mouse is more efficient for basic tool selection tasks. Estimated learning times for each input method indicate gestural control takes about 30 seconds longer to learn than traditional interaction methods.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The regular 2D mouse does not fit well for VR, as it has only 2 degrees-of-freedom (DoF) which is incompatible with the VR interaction requirements [50-52].”
From Trade-Off between Task Accuracy, Task Completion Time and Naturalness for Direct Object Manipulation in Virtual Reality (2022) · cites this paper as [52]
Citations
Cites
Analysis of the Accuracy and Robustness of the Leap Motion Controller
Weichert, F., Bachmann, D., Rudak, B., & Fisseler, D. (2013) · Sensors · doi:10.3390/s130506380 · Cited by 913
Cited by
Sorted by citations
Kangas, J., Kumar, S. K., Mehtonen, H., Järnstedt, J., & Raisamo, R. (2022) · Multimodal Technologies and Interaction · doi:10.3390/mti6010006 · Cited by 42
Metadata
- Authors
- Thompson, F. V.
- Affiliations
- Iowa State University (US)
- Corresponding author
- Thompson, Frederick Victor
- Publisher
- Iowa State University
- Published
- 2014-01-01
- OpenAlex
- W2186517475
- Cited by
- 4
- References
- 1
- Citations by year
- 2022: 1 · 2017: 2 · 2016: 1
- Type
- dissertation
- Language
- en
- Open access
- closed
- Locations
- Iowa State University Digital Repository (Iowa State University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Interactive and Immersive Displays
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Keywords
- gesture-based interaction, computer-aided drafting, computer-aided engineering, object manipulation, hand tracking, gesture taxonomy, learning time, tool selection, virtual reality
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review