Xueming Fu

Southern University of Science and Technology, University of Science and Technology of China

Papers

Total Citations

H-Index

About

Xueming Fu is a leading researcher in the field of human-robot interaction, with a primary focus on intelligent prosthetics, exoskeleton control, and rehabilitation robotics. His work centers on decoding human motion intent from surface electromyography (sEMG) signals, aiming to create seamless, intuitive control for assistive devices. Fu’s major contributions include developing a muscle synergy-driven adaptive neuro-fuzzy inference system (ANFIS) for continuous knee joint movement prediction, a novel approach that explicitly models coordinated muscle activations to bridge the gap between biological signals and robotic motion. His most cited paper (2022, 48 citations) has established a new paradigm for sEMG-based human-machine interfaces. More recently, Fu introduced a gait cycle-inspired learning strategy for continuous joint trajectory prediction, and a broad learning system for robot-assisted mirror rehabilitation that achieves real-time adaptive control by sensing equivalent kinematics. These innovations directly address critical challenges in rehabilitation, enabling more natural, responsive, and patient-specific therapy. With a growing citation record and a clear trajectory toward clinically impactful solutions, Fu is shaping the future of intelligent, bio-inspired robotic assistance for motor recovery.