Shou-Yan Yu
Papers
2
Total Citations
7
H-Index
2
About
Shou-Yan Yu is a researcher advancing the field of biomechatronics and neural control, with a focus on human motion analysis and rehabilitation engineering. Her key research areas include surface electromyography (sEMG) signal processing, joint torque prediction, and the application of cerebellar model neural networks for movement classification. Yu’s major contributions center on developing intelligent algorithms to decode neuromuscular signals for prosthetic and exoskeleton control. Her 2019 study on classifying ankle eversion and inversion movements using sEMG and cerebellar model neural networks achieved 4 citations, demonstrating a novel approach to recognizing complex ankle motions with high muscle specificity. Building on this, her 2021 work on torque prediction of the ankle joint from sEMG using a recurrent cerebellar model neural network (3 citations) addressed a critical need in quantitative rehabilitation training, enabling more natural and responsive exoskeleton assistance. By leveraging non-invasive sEMG signals, Yu’s research bridges the gap between neural activity and mechanical actuation, offering practical solutions for assistive devices and clinical assessment. Her work is notable for integrating computational intelligence with physiological signal analysis, paving the way for smarter, adaptive rehabilitation technologies that can improve patient outcomes.
Research Focus
Key Achievements
Top Papers
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- 2