Hanzhe Li
Papers
11
Total Citations
131
H-Index
5
About
Hanzhe Li is a researcher specializing in neural signal processing, human-robot interaction, and rehabilitation robotics, with a particular focus on leveraging biosignals to enable intuitive control of assistive and exoskeleton devices. His most influential contribution, "Processing Surface EMG Signals for Exoskeleton Motion Control" (2020, 56 citations), established foundational methods for using surface electromyography (sEMG) to drive robotic assistive systems, addressing persistent challenges in noise reduction and accuracy. Building on this foundation, Li has pioneered the fusion of electroencephalogram (EEG) and sEMG signals to improve the detection of voluntary lower limb movement intention, most notably through a CNN-LSTM model that clarifies the internal relationship between these two signal types. His work on readiness potential-based intention detection and cross-domain EEG prediction reflects a commitment to making brain-computer interfaces practical for real-world exoskeleton control. Li has also contributed to adaptive rehabilitation control strategies and virtual reality-enhanced BCI paradigms to support active neurological engagement in recovery. With a growing body of work accumulating over 130 citations, his research bridges neuroscience, signal processing, and robotic engineering, offering meaningful advances toward more responsive and personalized rehabilitation technologies.
Research Focus
Key Achievements
Top Papers
- 1Processing Surface EMG Signals for Exoskeleton Motion Control56 citations · 2020
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- 8RP-based Voluntary Movement Intention Detection of Lower limb using CNN4 citations · 2020
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