Yongbai Liu
Papers
8
Total Citations
81
H-Index
5
About
Dr. Yongbai Liu is a leading researcher in intelligent rehabilitation robotics and human-robot interaction (HRI) control, with a focus on leveraging neural networks and bio-signal processing to restore mobility. His core contributions lie in developing noise-tolerant and robust control algorithms for both upper and lower limb rehabilitation robots, using surface electromyography (sEMG) signals to decode human motion intention. His most cited work (2022, 27 citations) introduces a zeroing neurodynamic algorithm that enables precise HRI control even under non-ideal, real-world conditions. Dr. Liu is also recognized for pioneering novel RBF neural network-based iterative learning and sliding model controllers for lower limb rehabilitation robots (2019, 17 and 7 citations), which provide asymptotic stability and strong robustness for passive patient training. His innovative estimation of sEMG-based joint movements via RBF networks (2019, 14 citations) has been foundational for continuous motion prediction. With over 80 citations across his top papers, Dr. Liu’s work directly addresses critical challenges in rehabilitation technology, advancing the field toward more adaptive, intuitive, and clinically viable robotic systems that can safely assist patients in regaining motor function.
Research Focus
Key Achievements
Top Papers
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