Lisheng Xu
Papers
3
Total Citations
134
H-Index
3
About
Lisheng Xu is a leading researcher in the fields of rehabilitation robotics, neural control, and human–machine interaction, with a particular focus on surface electromyogram (sEMG)-based motion estimation. His major contributions include pioneering deep learning approaches for continuous, proportional control of upper limb prostheses and assistive robots. Notably, his work on the SCA-LSTM deep learning framework for continuous joint angle estimation from sEMG signals has garnered 80 citations, marking a significant advance in intuitive, natural control of rehabilitation devices. Xu also developed novel feature extraction methods for deep learning-based continuous estimation, cited 32 times, which enhance the performance of simultaneous and proportional control (SPC) systems. In a highly cited study (22 citations), he investigated the modulation of muscle synergies under varying force and arm position constraints, providing critical insights for neurorehabilitation techniques. His research bridges computational modeling and clinical application, advancing the design of smarter, more adaptive prosthetics and exoskeletons. Xu’s work is essential reading for students and researchers aiming to develop next-generation assistive technologies that restore natural movement.
Research Focus
Key Achievements
Top Papers
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