Understanding Physical Breakdowns in Virtual Reality
March 20, 2024 Β· Declared Dead Β· π CHI Extended Abstracts
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Wen-Jie Tseng
arXiv ID
2404.00025
Category
cs.HC: Human-Computer Interaction
Citations
7
Venue
CHI Extended Abstracts
Last Checked
4 months ago
Abstract
Virtual Reality (VR) moves away from well-controlled laboratory environments into public and personal spaces. As users are visually disconnected from the physical environment, interacting in an uncontrolled space frequently leads to collisions and raises safety concerns. In my thesis, I investigate this phenomenon which I define as the physical breakdown in VR. The goal is to understand the reasons for physical breakdowns, provide solutions, and explore future mechanisms that could perpetuate safety risks. First, I explored the reasons for physical breakdowns by investigating how people interact with the current VR safety mechanism (e.g., Oculus Guardian). Results show one reason for breaking out of the safety boundary is when interacting with large motions (e.g., swinging arms), the user does not have enough time to react although they see the safety boundary. I proposed a solution, FingerMapper, that maps small-scale finger motions onto virtual arms and hands to enable whole-body virtual arm motions in VR to avoid physical breakdowns. To demonstrate future safety risks, I explored the malicious use of perceptual manipulations (e.g., redirection techniques) in VR, which could deliberately create physical breakdowns without users noticing. Results indicate further open challenges about the cognitive process of how users comprehend their physical environment when they are blindfolded in VR.
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