Genning Zhang

Nanjing University of Science and Technology

Papers

Total Citations

H-Index

About

Genning Zhang is a researcher at the forefront of human-robot interaction and biomedical signal processing, with a focus on advancing rehabilitation and assistive technologies. Their work centers on the accurate estimation of human joint motion—a critical challenge for ensuring safe and effective human-robot collaboration. Zhang’s most notable contribution, detailed in their 2024 paper "Estimation of Elbow Joint Angle from EMG and IMU measurements based on Graph Convolution Neural Network," introduces a novel approach that fuses surface electromyography (sEMG) and inertial measurement unit (IMU) data using graph convolutional neural networks. This method addresses the longstanding difficulty of achieving high-precision joint angle estimation from sEMG alone, offering a pathway to more responsive and intuitive robotic exoskeletons and prosthetics. While early in its citation trajectory, this work has already garnered 3 citations, signaling growing interest from peers in rehabilitation engineering and neural interfaces. Zhang’s research bridges machine learning and biomechanics, promising to enhance the safety and efficacy of robotic devices for performance enhancement and motor recovery. Their innovative use of graph-based deep learning marks a significant step toward seamless human-machine integration.