Papers
13
Total Citations
294
H-Index
10
About
Yanjuan Geng is a leading researcher in neural engineering and human-machine interaction, specializing in decoding motor intent from electromyography (EMG) signals for advanced prosthetic and rehabilitation systems. Her work centers on developing robust, intuitive control methods for robotic hands and stroke rehabilitation devices. Geng’s major contributions include pioneering a CNN-attention network for continuous finger kinematics estimation from sEMG (58 citations), and a robust sparse representation approach for myoelectric control that withstands real-world interference (46 citations). She has also advanced transfer learning to create cross-subject generic models for finger joint angle estimation (29 citations), significantly reducing calibration time for new users. Her research addresses critical challenges in EMG-pattern recognition, including the co-existing impacts of dynamic factors (52 citations) and the modulation of muscle synergies under varying force and arm positions (22 citations). Geng’s work has been widely cited for its practical impact on multifunctional prostheses and active motor training systems, with notable achievements in developing flexible hybrid sensors for in-situ gesture recognition. Her interdisciplinary approach bridges signal processing, machine learning, and rehabilitation engineering, making her a key figure in advancing human-machine interfaces for clinical and industrial applications.
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