Papers
2
Total Citations
50
H-Index
2
About
Minjae Kim is a leading researcher in the field of human–machine interaction, with a primary focus on surface electromyography (sEMG) for intuitive robotic control and prosthetic applications. His work addresses a critical challenge in the field: the degradation of gesture recognition accuracy when sEMG sensors rotate on the skin. Kim’s most influential contribution, the "Simple and Fast Compensation of sEMG Interface Rotation for Robust Hand Motion Recognition" (2018, 41 citations), introduced an efficient algorithm that maintains reliable hand motion classification despite sensor misalignment, significantly advancing the practicality of sEMG for teleoperation and assistive robotics. He further refined this approach in his work on "Muscle Activation Source Model-based sEMG Signal Decomposition and Recognition of Interface Rotation" (2018, 9 citations), which models underlying muscle activation sources to decompose mixed signals and improve robustness. By tackling the real-world problem of sensor shift, Kim’s research directly enhances the usability of myoelectric control systems, making them more resilient for daily use. His contributions are foundational for developing next-generation prosthetic hands and human–robot interfaces that can adapt to natural movement variability.
Research Focus
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Top Papers
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