Ki-Hee Park
Papers
2
Total Citations
151
H-Index
2
About
Ki-Hee Park is a leading researcher in the field of human-machine interaction, with a primary focus on myoelectric interfaces and wearable rehabilitation robotics. His work centers on decoding movement intention from electromyogram (EMG) biosignals, a critical challenge for enabling intuitive control of assistive devices like arm prosthetics. Park’s major contribution lies in pioneering the application of deep learning—specifically convolutional neural networks—to overcome the high inter-user variability that has long hindered practical myoelectric systems. His most influential paper, "Movement intention decoding based on deep learning for multiuser myoelectric interfaces" (2016), has garnered 148 citations, establishing a foundational framework for robust, user-adaptive control. This work demonstrates how deep feature learning can extract reliable movement patterns from EMG data, significantly advancing the usability of wearable rehabilitation robots. Park’s research directly bridges artificial intelligence and biomedical engineering, offering scalable solutions for personalized assistive technology. His achievements highlight a commitment to translating complex biosignal processing into real-world applications that improve quality of life for individuals with motor impairments.
Research Focus
Key Achievements
Top Papers
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