Papers
2
Total Citations
19
H-Index
2
About
Qun He is a researcher advancing the frontiers of intelligent robotics and human-machine interaction, with a primary focus on rehabilitation technology and motion intention recognition. He is best known for pioneering work in multimodal physiological signal fusion, as demonstrated in his highly cited 2024 paper "E²FNet: An EEG- and EMG-Based Fusion Network for Hand Motion Intention Recognition" (12 citations). This work addresses the critical challenge of assisting individuals with limb disorders by overcoming the limitations of single-signal rehabilitation systems, proposing a novel deep learning architecture that integrates electroencephalography and electromyography data for more robust and accurate motion decoding. He has also made significant contributions to robotic control optimization through his 2022 study "Joints Trajectory Planning of Robot Based on Slime Mould Whale Optimization Algorithm" (7 citations), which introduces a bio-inspired metaheuristic to enhance the convergence speed and global search capability of joint trajectory planning. By tackling both the sensing and control pillars of modern robotics, He's research is directly shaping the development of more responsive, efficient, and accessible assistive technologies. His work is essential reading for students and researchers in rehabilitation engineering, human-robot interaction, and intelligent control systems.
Research Focus
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