Papers
6
Total Citations
12
H-Index
2
About
Xiangxu Qu is a robotics researcher focused on rehabilitation and assistive technologies, with key contributions in flexible joint design, cable-driven mechanisms, and human-robot interaction. Their work centers on developing variable stiffness joints and cable-driven robots for lower limb recovery training, addressing critical challenges in safe, adaptive rehabilitation. Notable achievements include the design of a flexible joint with active and passive stiffness adjustment based on torsion springs, and a cable-driven articulated robot for lower limb therapy, each garnering 3 citations. More recently, Qu has advanced calibration-free surface EMG (sEMG) intention recognition for upper-limb rehabilitation, proposing a novel framework that couples self-supervised pretraining with adversarial domain alignment—a breakthrough that eliminates the need for user-specific calibration. This work, along with their analysis of workspace and stiffness methods for cable-driven rehabilitation robots, demonstrates a systematic approach to improving robot dynamics and usability. With papers spanning 2019 to 2025, Qu’s research is steadily gaining recognition, contributing to the growing field of rehabilitation robotics by enhancing robot adaptability, safety, and patient outcomes.
Research Focus
Key Achievements
Top Papers
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