Papers
13
Total Citations
235
H-Index
7
About
Leigang Zhang is a prominent robotics and rehabilitation engineering researcher whose work sits at the intersection of human-robot interaction, adaptive control systems, and upper limb motor recovery. His research focuses primarily on developing intelligent end-effector rehabilitation robots and advanced control strategies to support patients with motor dysfunction, including stroke survivors. Zhang's most significant contributions center on assist-as-needed (AAN) control frameworks, which dynamically adjust robotic assistance based on a patient's real-time performance. His 2020 papers introducing impedance-based AAN controllers — together accumulating over 145 citations — established foundational approaches enabling robots to fluidly transition between passive, assistive, active, and resistive training modes. This work aligns rehabilitation robotics more closely with evidence-based motor relearning principles, enhancing therapeutic outcomes. Beyond control theory, Zhang has advanced kinematic dexterity analysis for human-robot interaction, trajectory optimization, and quantitative motor ability evaluation, enabling more objective and therapist-independent clinical assessment. His development of bilateral rehabilitation systems further reflects a commitment to naturalistic, neurologically informed training paradigms. With over 225 total citations and a research trajectory spanning hardware development, control algorithms, and clinical evaluation, Zhang has established himself as an impactful voice in the rapidly growing field of rehabilitative robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9Force-Field based assisted control for upper-limb rehabilitation robots4 citations · 2024
- 10