Wenjuan Zhong
Papers
5
Total Citations
87
H-Index
4
About
Wenjuan Zhong is a biomedical and rehabilitation engineering researcher whose work sits at the intersection of human movement science, electromyography (sEMG), and intelligent robotic systems. Her research focuses primarily on decoding human motor intent from physiological signals to enable seamless, adaptive control of assistive technologies such as exoskeletons, orthoses, and prosthetic limbs. Zhong's most influential contribution, "A Muscle Synergy-Driven ANFIS Approach to Predict Continuous Knee Joint Movement" (2022, 48 citations), introduced a novel framework combining muscle synergy modeling with adaptive neuro-fuzzy inference to accurately predict knee joint trajectories—a significant advance for exoskeleton control. Building on this, her 2024 work on multidimensional sEMG feature learning for locomotion mode prediction (19 citations) further enhances the intelligence of walking-assistive devices. Beyond lower-limb applications, she has investigated bilateral hand force coordination in dynamic bimanual tasks (13 citations) and developed specialized trajectory planning methods for ankle ligament rehabilitation robotics. Her gait cycle-inspired learning strategy for continuous motion prediction reflects a growing sophistication in physiologically grounded machine learning. Collectively, Zhong's research meaningfully advances human-robot interaction and personalized rehabilitation engineering.
Research Focus
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Top Papers
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