Comparing Pedestrian Navigation Methods in Virtual Reality and Real Life
October 06, 2020 Β· Declared Dead Β· π International Conference on Multimodal Interaction
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Gian-Luca Savino, Niklas Emanuel, Steven Kowalzik, Felix A. Kroll, Marvin C. Lange, Matthis Laudan, Rieke Leder, Zhanhua Liang, Dayana Markhabayeva, Martin SchmeiΓer, Nicolai SchΓΌtz, Carolin Stellmacher, Zihe Xu, Kerstin Bub, Thorsten Kluss, Jaime Maldonado, Ernst Kruijff, Johannes SchΓΆning
arXiv ID
2010.02561
Category
cs.HC: Human-Computer Interaction
Citations
27
Venue
International Conference on Multimodal Interaction
Last Checked
4 months ago
Abstract
Mobile navigation apps are among the most used mobile applications and are often used as a baseline to evaluate new mobile navigation technologies in field studies. As field studies often introduce external factors that are hard to control for, we investigate how pedestrian navigation methods can be evaluated in virtual reality (VR). We present a study comparing navigation methods in real life (RL) and VR to evaluate if VR environments are a viable alternative to RL environments when it comes to testing these. In a series of studies, participants navigated a real and a virtual environment using a paper map and a navigation app on a smartphone. We measured the differences in navigation performance, task load and spatial knowledge acquisition between RL and VR. From these we formulate guidelines for the improvement of pedestrian navigation systems in VR like improved legibility for small screen devices. We furthermore discuss appropriate low-cost and low-space VR-locomotion techniques and discuss more controllable locomotion techniques.
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