Zhufeng Lu
Papers
11
Total Citations
68
H-Index
5
About
Zhufeng Lu is a researcher specializing in brain-computer interfaces (BCI), neural signal processing, and human-robot interaction, with a particular focus on decoding human movement intention from physiological signals. His work bridges neuroscience and robotics engineering, contributing meaningfully to the development of intelligent exoskeletons, rehabilitation systems, and teleoperated robots. Lu's most significant contributions center on the fusion of electroencephalogram (EEG) and electromyography (EMG) signals to accurately detect voluntary movement intentions in the lower limbs. His most-cited paper (2021, 16 citations) investigated the homological characteristics of these signals, while a follow-up study (2022, 12 citations) introduced a CNN-LSTM deep learning model to improve fusion-based movement detection. His work also extends to spinal cord injury rehabilitation, where he applied deep learning to decode hand movement intentions in disabled patients, and to wrist joint angle estimation using surface EMG and neural networks. Beyond motor intention recognition, Lu has explored mental state monitoring during human-machine interaction, developing personalized speed adaptation methods for teleoperated robots and examining neuroplasticity under high-demand operational conditions. His cumulative citation record reflects a growing influence across rehabilitation robotics, BCI paradigm design, and adaptive human-machine systems — areas of increasing importance in both clinical and industrial applications.
Research Focus
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Top Papers
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- 8RP-based Voluntary Movement Intention Detection of Lower limb using CNN4 citations · 2020
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