Redirected Walking in Infinite Virtual Indoor Environment Using Change-blindness
December 28, 2022 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
June-Young Hwang, Soon-Uk Kwon, Yong-Hun Cho, Sang-Bin Jeon, In-Kwon Lee
arXiv ID
2212.13733
Category
cs.HC: Human-Computer Interaction
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
Abstract
We present a change-blindness based redirected walking algorithm that allows a user to explore on foot a virtual indoor environment consisting of an infinite number of rooms while at the same time ensuring collision-free walking for the user in real space. This method uses change blindness to scale and translate the room without the user's awareness by moving the wall while the user is not looking. Consequently, the virtual room containing the current user always exists in the valid real space. We measured the detection threshold for whether the user recognizes the movement of the wall outside the field of view. Then, we used the measured detection threshold to determine the amount of changing the dimension of the room by moving that wall. We conducted a live-user experiment to navigate the same virtual environment using the proposed method and other existing methods. As a result, users reported higher usability, presence, and immersion when using the proposed method while showing reduced motion sickness compared to other methods. Hence, our approach can be used to implement applications to allow users to explore an infinitely large virtual indoor environment such as virtual museum and virtual model house while simultaneously walking in a small real space, giving users a more realistic experience.
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