Bingzhu Wang
Papers
4
Total Citations
44
H-Index
3
About
Bingzhu Wang is a leading researcher in rehabilitation robotics and intelligent human–machine interaction, with a focus on lower limb motion recognition and control. His work integrates surface electromyography (sEMG) signals with advanced robotic systems to enable real-time, personalized rehabilitation training. Wang’s most cited paper, “Lower limb motion recognition based on surface electromyography signals and its experimental verification on a novel multi-posture lower limb rehabilitation robot” (2022, 19 citations), demonstrates his ability to bridge signal processing and robotic actuation for clinical applications. He further advanced this field by developing an online pattern recognition method for sEMG-based movement classification, enabling real-time adaptive therapy. Wang also contributed to soft robotics with his numerical investigation of a novel 3D soft pneumatic actuator (SPA) for multi-environment gripping (2022, 18 citations). In control engineering, he proposed a fractional-order PIλDμ controller combined with an IIMO-BP neural network for precise tracking in rehabilitation robots. Collectively, his work has garnered over 44 citations, reflecting its growing impact on assistive robotics and neurorehabilitation. Wang’s interdisciplinary approach—merging biomechanics, machine learning, and control theory—positions him as a key innovator in developing intelligent, patient-specific rehabilitation technologies.
Research Focus
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Top Papers
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