Xugang Xi
Papers
2
Total Citations
62
H-Index
2
About
Xugang Xi is a leading researcher in biomedical signal processing and human motion analysis, with a primary focus on surface electromyography (sEMG) for rehabilitation engineering. His major contributions lie in developing novel predictive models that translate sEMG signals into accurate knee-joint angle estimations, a critical challenge for intelligent prosthetics and robotic exoskeletons. In two highly cited 2019 studies (each garnering 31 citations), Xi introduced innovative approaches: one employing a multifeature extraction framework combined with a predictive model to decode motor intentions from multichannel sEMG, and the other pioneering the use of correlation dimension of wavelet coefficients (WCCD) with an Elman neural network to establish robust sEMG-to-angle relationships. These works demonstrate his expertise in fusing nonlinear dynamics, wavelet analysis, and machine learning to solve real-world rehabilitation problems. By enabling more intuitive control of assistive devices through natural muscle signals, Xi’s research directly impacts the development of smarter, adaptive therapies for patients with mobility impairments. His work represents a significant step toward seamless human-machine interfaces in clinical and wearable applications.
Research Focus
Key Achievements
Top Papers
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