Papers
3
Total Citations
24
H-Index
2
About
Jingyan Meng is a rising researcher in the field of rehabilitation robotics, with a focus on developing intelligent control systems for upper-limb therapy. Her work centers on creating safer, more adaptive human-robot interactions for stroke rehabilitation, addressing the critical challenge of balancing robotic assistance with patient engagement. Meng’s most cited paper (19 citations) introduces a fuzzy adaptive passive control strategy for end-effector rehabilitation robots, designed to prevent over-assistance by dynamically adjusting support based on patient performance. She has also pioneered the integration of error-modulated visual and haptic feedback to enhance motor learning and motivation, exploring how multimodal feedback fusion can improve rehabilitation outcomes. Additionally, Meng developed a compliance control strategy that estimates physical human-robot interaction forces in real time without requiring force sensors, significantly improving training safety and reducing hardware complexity. Her contributions are particularly notable for advancing patient-centered, sensor-less control methods that promote active participation during therapy. With her work published in 2023, Meng is quickly establishing herself as an innovator in making robot-assisted rehabilitation more intuitive, responsive, and effective for stroke survivors.
Research Focus
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Top Papers
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