Junhong Wang

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About

Dr. Junhong Wang is a leading researcher in biomedical signal processing and human motion analysis, with a particular focus on non-invasive techniques for rehabilitation and assistive technologies. Her most cited work, "Surface Electromyography Based Estimation of Knee Joint Angle by Using Correlation Dimension of Wavelet Coefficient" (2019, 31 citations), represents a significant contribution to the field of human-machine interfaces. In this seminal paper, Dr. Wang developed a novel regression model that establishes a robust relationship between surface electromyography (sEMG) signals and knee joint angles. Her innovative approach combines the correlation dimension of wavelet coefficients (WCCD) with an Elman neural network, creating a powerful estimation framework that captures the complex, nonlinear dynamics of muscle activity. Through careful experimental validation, she demonstrated the model's effectiveness in accurately predicting joint kinematics from muscle signals, offering promising applications for intelligent prosthetics, exoskeletons, and rehabilitation robotics. This work has been widely recognized for advancing the practical use of sEMG in real-time motion control systems. Dr. Wang's research continues to push the boundaries of how biological signals can be harnessed to restore and enhance human movement, making her a respected voice in the intersection of signal processing, biomechanics, and neural engineering.