Content Transfer Across Multiple Screens with Combined Eye-Gaze and Touch Interaction -- A Replication Study
October 24, 2022 Β· Declared Dead Β· π 2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Verena Biener, Jens Grubert
arXiv ID
2210.13283
Category
cs.HC: Human-Computer Interaction
Citations
1
Venue
2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)
Last Checked
4 months ago
Abstract
In this paper, we describe the results of replicating one of our studies from two years ago which compares two techniques for transferring content across multiple screens in VR. Results from the previous study have shown that a combined gaze and touch input can outperform a bimanual touch-only input in terms of task completion time, simulator sickness, task load and usability. Except for the simulator sickness, these findings could be validated by the replication. The difference with regards to simulator sickness and variations in absolute scores of the other measures could be explained by a different set of user with less VR 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