Usability of the Size, Spacing, and Depth of Virtual Buttons on Head-Mounted Displays
September 16, 2018 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Kyudong Park, Dohyeon Kim, Sung H. Han
arXiv ID
1809.05833
Category
cs.HC: Human-Computer Interaction
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
Virtual reality (VR) allows users to see and manipulate virtual scenes and items through input devices, like head-mounted displays. In this study, the effects of button size, spacing, and depth on the usability of virtual buttons in VR environments were investigated. Task completion time, number of errors, and subjective preferences were collected to test different levels of the button size, spacing, and depth. The experiment was conducted in a desktop setting with Oculus Rift and Leap motion. A total of 18 subjects performed a button selection task. The optimal levels of button size and spacing within the experimental conditions are 25 mm and between 5 mm and 9 mm, respectively. Button sizes of 15 mm with 1-mm spacing were too small to be used in VR environments. A trend of decreasing task completion time and the number of errors was observed as button size and spacing increased. However, large size and spacing may cause fatigue, due to continuous extension of the arms. For depth effects, the touch method took a shorter task completion time. However, the push method recorded a smaller number of errors, owing to the visual push-feedback. In this paper, we discuss advantages and disadvantages in detail. The results can be applied to many different application areas with VR HMD.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted