Demo Data from EDBT 2026 / reviewers in the wild / expert
Tarun Murugesan
dblp:387/7893
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 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 |
Haptics and multimodal interaction · 61% Human-robot interaction · 30% Health and well-being technologies · 9% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Haptics and multimodal interaction › haptic feedback
force feedback |
0.9 | 1 | 2025 | Haptic Shoulder for Rendering Biomechanically Accurate Joint Limits for Human-Robot Physical Interactions · ICRA 2025 |
Haptics and multimodal interaction
haptic interface |
0.9 | 1 | 2025 | Haptic Shoulder for Rendering Biomechanically Accurate Joint Limits for Human-Robot Physical Interactions · ICRA 2025 |
Human-robot interaction
physical human-robot interaction |
0.9 | 1 | 2025 | Haptic Shoulder for Rendering Biomechanically Accurate Joint Limits for Human-Robot Physical Interactions · ICRA 2025 |
Health and well-being technologies › rehabilitation
physical therapy |
0.3 | 1 | 2025 | Haptic Shoulder for Rendering Biomechanically Accurate Joint Limits for Human-Robot Physical Interactions · ICRA 2025 |
Methods — techniques the papers use, named apart from their topics
biomechanical modeling · 0.9CAD design · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Haptic Shoulder for Rendering Biomechanically Accurate Joint Limits for Human-Robot Physical InteractionsabstractHuman-robot physical interaction (pHRI) is a rapidly evolving research field with significant implications for physical therapy, search and rescue, and telemedicine. However, a major challenge lies in accurately understanding human constraints and safety in human-robot physical experiments without an IRB and physical human experiments. Concerns regarding human studies include safety concerns, repeatabil-ity, scalability of the number, and diversity of participants. This paper examines whether a physical approximation can serve as a stand-in for human subjects to enhance robot autonomy for physical assistance. This paper introduces the SHULDRD (Shoulder Haptic Universal Limb Dynamic Repositioning Device), an economical and anatomically similar device designed for real-time testing and deploying pHRI planning tasks on robots in the real world. SHULDRD replicates human shoulder motion, providing crucial force feedback and safety data. The device's open-source CAD and software facilitate easy construction and use, ensuring broad accessibility for researchers. By providing a flexible platform able to emulate infinite human subjects, ensure repeatable trials, and provide quantitative metrics to assess the effectiveness of the robotic intervention, SHULDRD aims to improve the safety and efficacy of human-robot physical interactions. Project URL: https://sites.google.com/view/haptic-shoulder/home Elizabeth Peiros, Calvin Joyce, Tarun Murugesan, Roger Nguyen, Isabella Fiorini, Rizzi Galibut, Michael C. Yip |
ICRA | 3 |