Eun-Ju Ha

Papers

Total Citations

231

H-Index

About

Eun-Ju Ha is a leading researcher at the intersection of assistive robotics and computer vision, with a primary focus on developing intelligent systems for elderly care and human-robot interaction. Her most influential work, "Human Pose Estimation Using MediaPipe Pose and Optimization Method Based on a Humanoid Model" (2023, 228 citations), addresses a critical societal challenge: monitoring seniors living alone who are at risk of falls. Ha pioneered a novel approach that overcomes the limitations of conventional deep learning methods by integrating MediaPipe's real-time pose estimation with a humanoid model-based optimization, enabling more accurate pose recognition even for partially occluded or absent body parts. This breakthrough has significant implications for mobile robots that can autonomously monitor and respond to emergencies. In her related work on gait measurement systems using human-following mobile robots, Ha further demonstrates her commitment to practical, deployable solutions for aging populations. Her research uniquely combines open-source tools with sophisticated optimization techniques, making advanced assistive technology more accessible and reliable. With her work gaining rapid recognition, Ha is establishing herself as a key innovator in creating safer, more responsive environments for vulnerable individuals through intelligent robotic systems.