Papers
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Total Citations
6
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About
Hyeonuk Nam is a researcher at the forefront of human-robot interaction, with a specialized focus on real-time sound recognition systems for assistive robotics. His work addresses a critical gap in robotic perception: the challenge of recognizing custom sound events—user-defined sounds whose acoustic properties vary individually, unlike standard environmental noises. Nam’s major contribution lies in developing adaptive recognition frameworks that enable human care robots to accurately interpret these personalized auditory cues in dynamic, real-life settings. His most cited paper, “Real-Time Sound Recognition System for Human Care Robot Considering Custom Sound Events” (2024), has already garnered 6 citations, signaling its growing influence in the robotics and audio processing communities. By tackling the practical limitations of conventional sound recognition, Nam’s research enhances the reliability and responsiveness of care robots, making them more intuitive and effective in supporting elderly or disabled users. His work represents a vital step toward truly context-aware robotic assistants, blending acoustic engineering with human-centered design. As the demand for intelligent care solutions rises, Nam’s contributions are poised to shape the next generation of socially aware robots.
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