Menghan Xiao
Papers
4
Total Citations
83
H-Index
3
About
Menghan Xiao is a pioneering researcher in rehabilitation robotics, specializing in human-robot interaction systems for upper limb exoskeletons. Her work addresses critical challenges in stroke and injury rehabilitation by developing intelligent systems that can detect patient intent and fatigue during therapy. Her most cited paper (2020, 48 citations) introduces a motion intent recognition system using altitude signal sensors and an adaptive Kalman filter, significantly improving the natural interaction between patients and robotic exoskeletons. She further advanced the field with a novel multi-information fusion method for fatigue detection (2020, 25 citations), enabling personalized rehabilitation training regimens. Xiao's technical contributions include a closed-loop PD iterative learning control method (2021) that achieves trajectory errors under 0.05 radians after just three iterations, and innovative variable stiffness actuators using magnetorheological fluid for safer human-robot interaction. Her research bridges control theory, biomechanics, and human factors engineering, with cumulative citations exceeding 80. Xiao's work is particularly notable for its practical implementation—she has developed and tested a six-degree-of-freedom upper limb exoskeleton prototype, demonstrating real-world applicability. Her contributions are essential reading for anyone working in rehabilitation robotics, human-robot interaction, or assistive technology.
Research Focus
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Top Papers
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