Demo Data from SIGIR 2026 / reviewers in the wild / expert
Kenya Sakamoto
dblp:265/2867
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
Wearable and physiological sensing · 100% | |
| Computer graphics and multimedia
1 paper |
Virtual and augmented reality · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Wearable and physiological sensing › eye tracking
gaze-based interaction |
0.8 | 1 | 2024 | EyeShadows: Peripheral Virtual Copies for Rapid Gaze Selection and Interaction · VR 2024 |
Wearable and physiological sensing › eye tracking › gaze-based interaction
gaze-based selection |
0.8 | 1 | 2024 | EyeShadows: Peripheral Virtual Copies for Rapid Gaze Selection and Interaction · VR 2024 |
Virtual and augmented reality
augmented reality |
0.2 | 1 | 2024 | EyeShadows: Peripheral Virtual Copies for Rapid Gaze Selection and Interaction · VR 2024 |
Methods — techniques the papers use, named apart from their topics
user study · 1.5performance evaluation · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | EyeShadows: Peripheral Virtual Copies for Rapid Gaze Selection and InteractionabstractIn eye-tracked augmented and virtual reality (AR/VR), instantaneous and accurate hands-free selection of virtual elements is still a significant challenge. Though other methods that involve gaze-coupled head movements or hovering can improve selection times in comparison to methods like gaze-dwell, they are either not instantaneous or have difficulty ensuring that the user’s selection is deliberate. In this paper, we present EyeShadows, an eye gaze-based selection system that takes advantage of peripheral copies (shadows) of items that allow for quick selection and manipulation of an object or corresponding menus. This method is compatible with a variety of different selection tasks and controllable items, avoids the Midas touch problem, does not clutter the virtual environment, and is context sensitive. We have implemented and refined this selection tool for VR and AR, including testing with optical and video see-through (OST/VST) displays. Moreover, we demonstrate that this method can be used for a wide range of AR and VR applications, including manipulation of sliders or analog elements. We test its performance in VR against three other selection techniques, including dwell (baseline), an inertial reticle, and head-coupled selection. Results showed that selection with EyeShadows was significantly faster than dwell (baseline), outperforming in the select and search and select tasks by 29.8% and 15.7%, respectively, though error rates varied between tasks. Jason Orlosky, Chang Liu 0081, Kenya Sakamoto, Ludwig Sidenmark, Adam Mansour |
VR | 3 |