Papers
3
Total Citations
148
H-Index
3
About
Weiyu Guo is a leading researcher in the field of human–robot interaction and assistive technologies, with a primary focus on surface electromyography (sEMG)-based control systems. Their work centers on enabling continuous, proportional, and intuitive control of prosthetic limbs and rehabilitation robots, moving beyond traditional discrete motion commands. Guo’s major contributions include pioneering the use of deep learning architectures—specifically Long Short-Term Memory (LSTM) networks and Large-Scale Temporal Convolutional Networks (TCN)—for the real-time, continuous estimation of grasp movements and finger kinematics from sEMG signals. Their most-cited paper (2020, 79 citations) established a novel LSTM-based framework for this purpose, while subsequent work (2021, 37 and 32 citations) advanced the field by introducing efficient feature extraction methods and temporal convolutional models. These innovations significantly improve the naturalness and accuracy of man–machine interfaces, directly impacting the development of smarter, more responsive assistive robots and prostheses. Guo’s research is highly influential, providing foundational methods for simultaneous and proportional control that are critical for next-generation rehabilitation technologies.
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