Author ORCID Identifier
https://orcid.org/0009-0004-0065-8600
Document Type
Conference Paper
Disciplines
1.2 COMPUTER AND INFORMATION SCIENCE, Robotics and automatic control, Health care sciences and services
Abstract
Medical interpreters are crucial in facilitating communication between healthcare stakeholders who speak different languages. Body-oriented gestures convey critical information essential for accurate and high-quality interpretation in healthcare settings. This study introduces a body-oriented gesture generation system designed for medical interpreter robots based on reinforcement learning from human feedback (RLHF). The system allows robots to interpret more naturally and improve over time through interactions with humans. By adopting a human-centered development approach, we tailor our system to address the actual needs of healthcare stakeholders.
DOI
https://doi.org/10.21427/qmjy-9265
Recommended Citation
Ngo, Tung; Murphy, Emma; McGinn, Conor; and Ross, Robert, "Body-Oriented Gesture Generation System for Medical Interpreter Robots Based on Reinforcement Learning from Human Feedback" (2024). Conference papers. 5.
https://arrow.tudublin.ie/adaptcon/5
Funder
ADAPT Center
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Included in
Communication Commons, Computer Sciences Commons, Medicine and Health Sciences Commons, Other Social and Behavioral Sciences Commons
Publication Details
Artificial Intelligence in Healthcare First International Conference, AIiH 2024, Swansea, UK, September 4–6, 2024.
doi:10.21427/qmjy-9265