Zi-Qin Ling
Papers
3
Total Citations
27
H-Index
2
About
Zi-Qin Ling is a leading researcher at the forefront of intelligent rehabilitation robotics, specializing in human-robot interaction, biosignal processing, and adaptive control systems. His work focuses on decoding surface electromyography (sEMG) signals to enable seamless, intuitive control of lower limb exoskeletons for patients with mobility impairments. Ling’s major contributions include developing a real-time knee joint angle estimation method using Back Propagation Neural Networks (BPNN), which achieved 13 citations for its potential to enhance continuous motion control in rehabilitation. He also pioneered a domain adaptive convolutional neural network for gait phase recognition, cited 12 times, that overcomes performance degradation caused by varying walking speeds—a critical step toward practical, user-friendly prosthetics. Additionally, Ling advanced robust exoskeleton control with an adaptive backstepping sliding mode controller incorporating a combined double power reaching law, addressing challenges of nonlinearity and external disturbances. His work has been recognized for bridging the gap between laboratory algorithms and real-world clinical applications, with cumulative citations reflecting growing impact. Ling’s research not only pushes the boundaries of neural-machine interfaces but also holds promise for restoring mobility and independence to individuals with lower limb disabilities.
Research Focus
Key Achievements
Top Papers
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