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Total Citations
26
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About
Dr. Geng Liu is a leading researcher in human-machine interaction and wearable robotics, with a particular focus on leveraging surface electromyography (sEMG) for intuitive motion control. His most cited work, "sEMG-Based Continuous Estimation of Knee Joint Angle Using Deep Learning with Convolutional Neural Network" (2019, 26 citations), represents a significant breakthrough in the field. Dr. Liu pioneered the use of deep learning architectures—specifically convolutional neural networks—to decode sEMG signals for continuous, real-time estimation of joint kinematics, moving beyond traditional discrete gesture recognition. This approach enables more natural and responsive control of lower-limb assistive devices, such as exoskeletons and prosthetics, by predicting user intent before movement occurs. His contributions directly address the critical challenge of achieving seamless human-machine synergy in rehabilitation and augmentation technologies. By demonstrating that sEMG signals can be reliably translated into continuous joint angles, Dr. Liu’s work has laid a foundational framework for next-generation wearable robots that adapt fluidly to human motion, enhancing both clinical outcomes and user experience.
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Top Papers
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