134 citations · 267 across the 10 of their papers we have counts for
10 papers
In Sync: Exploring Synchronization to Increase Trust Between Humans and Non-humanoid Robots
Wieslaw Bartkowski, Andrzej Nowak, Filip Ignacy Czajkowski +2
When we go for a walk with friends, we can observe an interesting effect: From step lengths to arm movements - our movements unconsciously align; they synchronize. Prior research f…
Understanding the Uncertainty Loop of Human-Robot Interaction
Jan Leusmann, Chao Wang, Michael Gienger +2
Recently the field of Human-Robot Interaction gained popularity, due to the wide range of possibilities of how robots can support humans during daily tasks. One form of supportive…
The Walking Talking Stick: Understanding Automated Note-Taking in Walking Meetings
Luke Haliburton, Natalia Bartłomiejczyk, Paweł W. Woźniak +2
While walking meetings offer a healthy alternative to sit-down meetings, they also pose practical challenges. Taking notes is difficult while walking, which limits the potential of…
Feeling the Temperature of the Room: Unobtrusive Thermal Display of Engagement during Group Communication
Luke Haliburton, Svenja Yvonne Schött, Linda Hirsch +2
Thermal signals have been explored in HCI for emotion-elicitation and enhancing two-person communication, showing that temperature invokes social and emotional signals in individua…
Short-Form Videos Degrade Our Capacity to Retain Intentions: Effect of Context Switching On Prospective Memory
Francesco Chiossi, Luke Haliburton, Changkun Ou +2
Social media platforms use short, highly engaging videos to catch users' attention. While the short-form video feeds popularized by TikTok are rapidly spreading to other platforms,…
Investigating Labeler Bias in Face Annotation for Machine Learning
Luke Haliburton, Sinksar Ghebremedhin, Robin Welsch +2
In a world increasingly reliant on artificial intelligence, it is more important than ever to consider the ethical implications of artificial intelligence on humanity. One key unde…
EEG2Vec: Learning Affective EEG Representations via Variational Autoencoders
David Bethge, Philipp Hallgarten, Tobias Grosse-Puppendahl +4
There is a growing need for sparse representational formats of human affective states that can be utilized in scenarios with limited computational memory resources. We explore whet…
Exploiting Multiple EEG Data Domains with Adversarial Learning
David Bethge, Philipp Hallgarten, Ozan Özdenizci +3
Electroencephalography (EEG) is shown to be a valuable data source for evaluating subjects' mental states. However, the interpretation of multi-modal EEG signals is challenging, as…
The Placebo Effect of Artificial Intelligence in Human-Computer Interaction
Thomas Kosch, Robin Welsch, Lewis Chuang +1
In medicine, patients can obtain real benefits from a sham treatment. These benefits are known as the placebo effect. We report two experiments (Experiment I: N=369; Experiment II:…
Domain-Invariant Representation Learning from EEG with Private Encoders
David Bethge, Philipp Hallgarten, Tobias Grosse-Puppendahl +4
Deep learning based electroencephalography (EEG) signal processing methods are known to suffer from poor test-time generalization due to the changes in data distribution. This beco…