EDBT 2026 Demo / reviewers in the wild / expert
Søren Kyllingsbæk
dblp:125/4253
· DBLP profile ↗
2ranked-venue papers
0as first author
1since 2021 · last 2021
0000-0002-4100-437XORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1
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 |
Immersive interaction · 44% Human-robot interaction · 44% Learning and educational technologies · 13% | |
| Computer graphics and multimedia
1 paper |
Virtual and augmented reality · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Virtual and augmented reality › virtual environment
immersive virtual environments |
0.5 | 1 | 2021 | Correction of Avatar Hand Movements Supports Learning of a Motor Skill · VR 2021 |
Immersive interaction
avatar |
0.5 | 1 | 2021 | Correction of Avatar Hand Movements Supports Learning of a Motor Skill · VR 2021 |
Human-robot interaction › physical human-robot interaction
motion guidance |
0.5 | 1 | 2021 | Correction of Avatar Hand Movements Supports Learning of a Motor Skill · VR 2021 |
Methods — techniques the papers use, named apart from their topics
user study · 1.0avatar movement correction · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Correction of Avatar Hand Movements Supports Learning of a Motor SkillabstractLearning to move the hands in particular ways is essential in many training andleisure virtual reality applications, yet challenging. Existing techniques that support learning of motor movement in virtual reality rely on external cues such as arrows showing where to move or transparent hands showing the target movement. We propose a technique where the avatar's hand movement is corrected to be closer to the target movement. This embeds guidance in the user's avatar, instead of in external cues and minimizes visual distraction. Through two experiments, we found that such movement guidance improves the short-term retention of the target movement when compared to a control condition without guidance. Klemen Lilija, Søren Kyllingsbæk, Kasper Hornbæk |
VR | 2 |
| 2012 | The Theory of Visual Attention without the race: a new model of visual selection
Tobias Andersen, Søren Kyllingsbæk |
CogSci | 2 |