Yan Zeng

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About

Yan Zeng is a researcher whose work sits at the dynamic intersection of human-robot interaction, rehabilitation robotics, and biomedical signal processing. With a focus on developing intelligent exoskeleton systems, Zeng has made meaningful contributions to the field of motor intent recognition and transparent control strategies for wearable robotic devices. His most recognized work, "Improving the Transparency of an Exoskeleton Knee Joint Based on the Understanding of Motor Intent Using Energy Kernel Method of EMG" (2016), has garnered 68 citations and addresses one of the central challenges in rehabilitation engineering: enabling robotic exoskeletons to respond naturally and adaptively to human movement. By applying the Energy Kernel Method to electromyographic (EMG) signals, Zeng advanced the capability of exoskeletons to operate in a "patient-in-charge" mode, a critical feature for late-phase rehabilitation where user autonomy is paramount. This work reflects a broader commitment to bridging neuromuscular science with robotic control design, ultimately improving the clinical usability of assistive technologies. Zeng's research holds significant promise for stroke survivors and individuals with motor impairments seeking effective, intuitive rehabilitation solutions.