Kunlu Gan
Papers
2
Total Citations
8
H-Index
2
About
Kunlu Gan is a researcher focused on the intersection of rehabilitation robotics, human-machine interaction, and biosignal processing. Their key contributions center on developing intelligent control systems that leverage electromyography (EMG) and multi-sensor fusion to enhance assistive and rehabilitative technologies. In their highly cited work, "A close-loop EMG model for continuous joint movements estimation of a rehabilitation robot," Gan introduced a novel closed-loop algorithm using an EMG state-space model to estimate continuous joint motion, enabling active, patient-driven control of lower limb rehabilitation robots—a critical step toward more responsive and effective therapy. This work has garnered 5 citations, reflecting its foundational impact. Further demonstrating their versatility, Gan’s study "Comparative Study on Gesture Recognition Using Multiple Kernel Learning via Multi-mode Information Fusion" (3 citations) pioneered a multi-mode fusion classifier that integrates inertial measurement units with surface EMG sensors for robust gesture recognition, achieving superior control for mobile robots beyond vision-based methods. This work highlights Gan’s expertise in combining physiological and inertial signals for intuitive human-robot interfaces. Through these contributions, Kunlu Gan has advanced the fields of rehabilitation engineering and gesture-based control, laying groundwork for more adaptive and natural assistive technologies.
Research Focus
Key Achievements
Top Papers
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