Weibo Wang
Papers
2
Total Citations
8
H-Index
2
About
Weibo Wang is a leading researcher in neural engineering and human-machine interaction, with a primary focus on high-density surface electromyography (HD-sEMG) and its applications in assistive robotics. His work addresses fundamental challenges in extracting clean, meaningful neural signals from noisy biological recordings. In his highly cited 2024 paper on variational mode decomposition (VMD), Wang developed a noise-specific adaptive removal method that systematically eliminates power line interference, white Gaussian noise, baseline wandering, and motion artifacts from HD-sEMG signals—a critical advancement for reliable motor unit (MU) decomposition. His equally influential work on exoskeleton neuromuscular interfaces introduces a novel framework using motor unit action potential (MUAP) models derived from HD-sEMG, enabling more intuitive and precise control of wearable robotic systems. By integrating 2-D microneedle electrode arrays with advanced signal processing, Wang has pushed the boundaries of noninvasive neural recording. His contributions are already shaping next-generation prosthetics and rehabilitation technologies, with his recent papers accumulating citations that underscore their immediate impact on the field.
Research Focus
Key Achievements
Top Papers
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