Go-Eum Cha
Papers
5
Total Citations
19
H-Index
3
About
Go-Eum Cha is a pioneering researcher at the intersection of human-robot interaction (HRI), affective computing, and cognitive workload assessment. Her work focuses on understanding and measuring the subtle, often unconscious, human responses that shape how we interact with robots and intelligent systems. Cha’s major contributions include the creation of the **ROSbag-based Multimodal Affective Dataset** (2020, 9 citations), a foundational resource that uses standardized affective stimuli (IAPS/IADS) to generate rich, multimodal data for studying emotional and cognitive states in robotic contexts. She further advanced the field by developing **SMART-TeleLoad** (2024), a novel GUI for generating affective loads in teleoperation studies, and by uncovering the correlation between **unconscious mouse actions and cognitive workload** (2022). Her recent work on **sentiment-based backchannels and active listening** (2025) demonstrates how robots can enhance self-disclosure and engagement by exhibiting socio-emotionally intelligent behaviors. Cha’s research is notable for its practical, data-driven approach to making robots more perceptive and responsive to human operators, with direct implications for safer, more intuitive teleoperation and socially assistive robotics.
Research Focus
Key Achievements
Top Papers
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