Demo Data from SIGIR 2026 / reviewers in the wild / expert
Nefeli Kousi
dblp:316/6087
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
0009-0001-6291-062XORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 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 |
Human-robot interaction · 54% Health and well-being technologies · 23% Design research and methods · 23% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-robot interaction
child-robot interaction |
0.6 | 1 | 2022 | Understanding Design Preferences for Robots for Pain Management: A Co-Design Study · HRI 2022 |
Health and well-being technologies
pain management |
0.6 | 1 | 2022 | Understanding Design Preferences for Robots for Pain Management: A Co-Design Study · HRI 2022 |
Design research and methods
participatory design |
0.6 | 1 | 2022 | Understanding Design Preferences for Robots for Pain Management: A Co-Design Study · HRI 2022 |
Human-robot interaction › assistive robotics
socially assistive robotics |
0.6 | 1 | 2022 | Understanding Design Preferences for Robots for Pain Management: A Co-Design Study · HRI 2022 |
Human-robot interaction › adaptive robot behavior
robot personalization |
0.2 | 1 | 2022 | Understanding Design Preferences for Robots for Pain Management: A Co-Design Study · HRI 2022 |
Methods — techniques the papers use, named apart from their topics
survey · 0.6interviews · 0.6co-design workshops · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Understanding Design Preferences for Robots for Pain Management: A Co-Design StudyabstractThere is growing interest in psychological interventions using socially assistive robots to mitigate distress and pain in the pediatric population. This work seeks to address the deficit in understanding of what features and functionality young children and their parents desire to help with pain management by using co-design, a common approach to exploring participants' imaginations and gathering design requirements. To close this gap, we carried out a co-design workshop involving seven families (with children aged between 4–6 and their parents) to understand their expectations and design preferences for a robot designed for pain management in children. Data were collected from surveys, video and audio recordings, interviews, and field notes. We present the robot prototypes constructed during the workshops and derive several preferences of the children (e.g, zoomorphic shape, distractors and emotional expressions as behaviors). Additionally, we report methodological insights regarding the involvement of young children and their parents in the co-design process. Based on the findings of this co-design study, we discuss personalization as a possible design concept for future child-robot interaction development. Feiran Zhang, Frank Broz, Edwin Dertien, Nefeli Kousi, Jules A. M. van Gurp, Oriana Isabella Ferrari, Ignacio Malagon, Emilia I. Barakova |
HRI | 4 |