Kuihua Geng
Papers
2
Total Citations
33
H-Index
2
About
Kuihua Geng is a researcher specializing in teleoperation, human-robot interaction, and signal processing, with a particular focus on mitigating physiological tremors that degrade precision in remote manipulation systems. Geng’s major contributions lie in developing advanced tremor-filtering models that integrate machine learning with wavelet-based signal analysis. Their most-cited work, “Broad learning extreme learning machine for forecasting and eliminating tremors in teleoperation” (2021, 24 citations), introduces a novel broad learning framework that efficiently predicts and cancels involuntary hand tremors in real-time teleoperation tasks. Building on this, Geng proposed the “Three-domain Wavelet Least Square Support Vector Machine” (2022, 9 citations), a hybrid model that fuses time, frequency, and wavelet-domain features to achieve robust tremor suppression under varying operational conditions. These contributions are critical for enhancing the safety and accuracy of teleoperated surgical robots and remote handling systems. Geng’s work bridges theoretical machine learning with practical robotics, offering computationally efficient solutions that outperform traditional filtering methods. Their research continues to influence the design of adaptive human-machine interfaces, making high-precision teleoperation more accessible in medical and industrial applications.
Research Focus
Key Achievements
Top Papers
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