Kang Kim
Papers
3
Total Citations
123
H-Index
3
About
Kang Kim is a leading researcher in neurorehabilitation engineering, specializing in the intersection of non-invasive sensing, human effort estimation, and assistive robotics. Their major contributions center on developing novel methods to accurately predict volitional joint effort in individuals with neurological impairments—a critical challenge for safe and effective human-robot interaction. Kim’s most-cited work (70 citations) introduced a groundbreaking approach combining ultrasound sonography with electromyography (EMG) to predict ankle dorsiflexion moment, overcoming the signal selectivity issues that plague traditional EMG alone. Expanding on this, their 2020 study (50 citations) systematically evaluated non-invasive ankle joint effort prediction methods, establishing a benchmark for integrating ultrasound imaging into neurorehabilitation control systems. Kim has also advanced observer design for nonlinear neuromuscular systems with multi-rate sampled and delayed outputs, directly supporting real-time control of robotic exoskeletons and functional electrical stimulation for spinal cord injury therapy. With over 120 combined citations, Kim’s work is foundational for developing safer, more responsive assistive devices that accurately interpret human intent. Their research bridges biomechanics, signal processing, and control theory, offering practical pathways to restore mobility in clinical populations.
Research Focus
Key Achievements
Top Papers
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