Papers
62
Total Citations
2,489
H-Index
21
About
Rong Song is a prominent researcher at the intersection of rehabilitation robotics, human-robot interaction, and biomedical engineering, whose work has fundamentally advanced how technology assists motor recovery and enables intuitive human-machine control. With a body of work accumulating over 1,500 citations, Song has made sustained contributions across more than a decade of rigorous research. Song's most celebrated contributions lie in electromyography (EMG)-driven robotic rehabilitation, pioneering systems that translate voluntary muscle signals into assistive robotic movement for stroke patients. His landmark 2009 study comparing EMG-driven robots with passive motion devices for wrist rehabilitation (210 citations) and his 2008 work on continuous myoelectric control for elbow training (196 citations) established foundational protocols now widely referenced in the field. His research consistently demonstrates that intention-driven, active participation accelerates motor recovery more effectively than passive approaches. Beyond stroke rehabilitation, Song has expanded his scope to exoskeleton control, sensory-motor fusion in robotic hand-eye systems, and cutting-edge gesture recognition using recurrent neural networks for teleoperated surgical robots (245 citations). His adaptive admittance control framework for ankle exoskeletons further showcases his ability to bridge biomechanical modeling with practical assistive technology. Song's work represents a compelling vision of empathetic, intelligent robotics that responds to the human body's own signals.
Research Focus
Key Achievements
Top Papers
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- 6The design and control of a 3DOF lower limb rehabilitation robot142 citations · 2015
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- 8Myoelectrically controlled wrist robot for stroke rehabilitation111 citations · 2013
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