Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data.

Søren Kyllingsbæk

dblp:125/4253 · DBLP profile ↗
← Back
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

TopicWeightPapersLastEvidence papers
Virtual and augmented reality › virtual environment
immersive virtual environments
0.512021
Correction of Avatar Hand Movements Supports Learning of a Motor Skill · VR 2021
Immersive interaction
avatar
0.512021
Correction of Avatar Hand Movements Supports Learning of a Motor Skill · VR 2021
Human-robot interaction › physical human-robot interaction
motion guidance
0.512021
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
YearPublicationVenuePosition
2021 Correction of Avatar Hand Movements Supports Learning of a Motor Skill
abstract
Learning 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
VR2
2012 The Theory of Visual Attention without the race: a new model of visual selection
Tobias Andersen, Søren Kyllingsbæk
CogSci2