Papers
22
Total Citations
465
H-Index
9
About
Xiaogang Hu is a leading researcher in neural-machine interfaces and myoelectric control, with a focus on restoring dexterous hand function for individuals with amputation, spinal cord injury, or stroke. His work bridges fundamental neuroscience and applied rehabilitation engineering, developing decoding algorithms that translate neural drive—specifically motoneuron discharge information—into continuous, concurrent control of robotic fingers. Hu’s major contributions include pioneering methods for the simultaneous prediction of finger forces, kinematics, and kinetics from neural activity, enabling more intuitive and natural control of prosthetic and assistive robotic hands. His highly cited review on myoelectric control of robotic lower limb prostheses (209 citations) has shaped the field’s understanding of control paradigms and challenges. With over 400 cumulative citations across his top papers, Hu’s research has advanced robust neural decoding under real-world conditions, including for stroke survivors. Notable achievements include developing unsupervised and generic neural network models for population-level neural activity, and achieving real-time, concurrent force control. His work is foundational for next-generation, neurally-driven assistive devices that restore natural hand function.
Research Focus
Key Achievements
Top Papers
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- 6Robust neural decoding for dexterous control of robotic hand kinematics19 citations · 2023
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