peer_reviewed: 3 entries

Publications

Peer-reviewed work from research at VU Amsterdam across computer vision and human-robot interaction.

2026
CONFERENCE
under review
Submitted to IDC '26 (ACM Interaction Design and Children)

The Dream Robot: A Social Robot for Delivering Medical Hypnosis to Children in Hospitals

Judith Weda, Mike E.U. Ligthart, Amke Elise Klompmaker, Anouk Neerincx, Sobhaan ul Husan, Sofie Veld, Fleur M. Hendriks, Mirjam de Haas, Arine Vlieger, Matthijs Smakman, Simone Marijke de Droog

Invasive medical procedures can cause significant anxiety in children, affecting both immediate and long-term healthcare experiences. This paper introduces the Dream Robot, a social robot designed to deliver a medical hypnosis intervention to reduce anxiety in pediatric hospitals. Through in-situ pilots during blood draws, anesthesia induction, and feeding tube placement, we examined how children, parents, and clinicians engaged with robot-guided medical hypnosis. Our findings indicate that such interventions can support children's coping, but only when aligned with developmental differences, procedural timing, and social dynamics between child, parent, and medical professionals.

HRISocial RoboticsMedical HypnosisPediatric Care
links
doi: pending
2026
CONFERENCE
peer-reviewed ✓
HRI '26

The Robot Bookworm: Fostering Children's Reading Motivation through Personalized Book Discussions

Elena Malnatsky, Kuhu Sinha, Sofie Veld, Sobhaan ul Husan, Rafaella van Nee, Daniel Wijnhorst, Shenghui Wang, Koen V. Hindriks, Mike E. U. Ligthart

A multi-session intervention co-designed with children and educators in which a social robot (Leo the Nao) assigned each child a personally fitting book and engaged them in pedagogically structured discussions. Personalized book-aligned dialogic content was generated offline by a language model and moderated by humans. The study compared a personalized book discussion condition with a book-neutral control across four sessions in two primary schools with approximately 100 children.

HRISocial RoboticsEducationNLP
links
→ doi
doi: 10.1145/3757279.3785618
2025
WORKSHOP
peer-reviewed ✓
MMSports '25

Semi-Automatic Estimation of Body Orientation in Football

Sobhaan ul Husan, Mauricio Verano Merino, Elixabete Sarasola Nieto

We present a computer vision pipeline for automatically estimating player body orientation during football pass events using pose estimation techniques. The pipeline was validated using video footage from a Women's Super League match between Brighton and Aston Villa (2024/25 season), achieving 75% accuracy on real-world match footage compared against professional analyst annotations. Body orientation is a crucial component for tactical analysis, allowing coaching staff to obtain insights into players' attention, decision-making, and strategic positioning.

Computer VisionSports AnalyticsPose EstimationFootball
links
→ doi
doi: 10.1145/3728423.3759407