Papers
10
Total Citations
114
H-Index
6
About
Sangho Kim is a robotics and human-robot interaction researcher whose work spans nearly three decades, from foundational robot kinematics to cutting-edge exoskeleton intelligence. His research centers on three interconnected domains: robotic systems design and control, wearable and collaborative robots for industrial applications, and the integration of machine learning for intuitive human-robot interaction. Kim's most recognized contribution involves façade-cleaning robot control systems, which has garnered 58 citations and demonstrates his early investment in applied autonomous robotics. More recently, his work on EMG-based CNN-LSTM models for predicting human lifting intentions represents a significant step toward eliminating control delays in industrial exoskeletons — a persistent barrier to their widespread adoption. His parallel investigations into exoskeleton usability standards and user-centered evaluation frameworks for collaborative robots reflect a distinctive commitment to bridging technological capability with real-world human safety needs. With contributions ranging from path planning algorithms for high-degree-of-freedom robots to force/torque sensing for humanoid balance control, Kim brings a broad, systems-level perspective to robotics research. Accumulating over 110 citations across his portfolio, his work meaningfully advances both the technical and ergonomic frontiers of human-centered robotics.
Research Focus
Key Achievements
Top Papers
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- 2Continuous Intention Prediction of Lifting Motions Using EMG-Based CNN-LSTM14 citations · 2024
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- 4Analytic generation of workspace using the robot kinematics8 citations · 1997
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