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About

Feng Ru is a researcher at the intersection of robotics, rehabilitation engineering, and human-machine interaction. Their work centers on developing intelligent robotic systems that mimic natural human movement for assistive and therapeutic applications. A key contribution is the integration of biomechanical data—such as human gait patterns and electromyography (EMG) signals—into robot control algorithms. For instance, their most-cited paper (5 citations) presents a method for generating walking trajectories for a 3D-printed biped robot by analyzing human natural gait and applying Zero Moment Point (ZMP) criteria, advancing the customization of legged robots. Ru also pioneered the use of cascaded Kinect and EMG gesture decoding for robot-aided hand neurorehabilitation (3 citations), emphasizing active, target-oriented training over passive repetition. Their work on an EMG-driven elbow exoskeleton (2 citations) further highlights a commitment to improving neurological recovery through active patient engagement. Additionally, Ru has explored repetitive control strategies for gait rehabilitation robots (2 citations), enabling periodic walking training. With a focus on translating human motor control principles into practical robotic systems, Feng Ru’s research holds promise for personalized rehabilitation and assistive technologies.