Papers
6
Total Citations
127
H-Index
5
About
Song Joo Lee is a leading researcher at the intersection of rehabilitation engineering and human-machine interaction. Her work focuses on decoding upper-limb movement intentions using electromyography (EMG) signals, with the goal of restoring function for individuals with neurological injuries. In her most cited work, she developed a model for continuous and simultaneous estimation of arm kinematics from EMG in both able-bodied individuals and stroke survivors (68 citations). She has also pioneered the use of convolutional neural networks to classify upper-limb reaching-to-grasping tasks for prosthetic hand control (23 citations). Beyond upper-limb applications, Lee has advanced rehabilitation for children with cerebral palsy by designing combined ankle/knee stretching and pivoting stepping training (15 citations), and has developed off-axis robotic trainers to improve neuromuscular control in knee injuries (11 citations). Her recent work includes multi-joint assessments of proprioception impairments post-stroke (7 citations). Through her innovative integration of robotics, signal processing, and clinical rehabilitation, Lee is shaping the future of assistive technologies and neurorehabilitation.
Research Focus
Key Achievements
Top Papers
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- 5Multi-joint Assessment of Proprioception Impairments Poststroke7 citations · 2023
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