Papers
1
Total Citations
21
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1
About
Zishan Han is a leading researcher in intelligent neuroprosthetics and human–machine interaction, with a core focus on pattern recognition and bionic manipulation driven by surface electromyography (sEMG) signals. His most cited work, "Pattern recognition and bionic manipulator driving by surface electromyography signals using convolutional neural network" (2018, 21 citations), represents a pivotal contribution to the field of robotic control for amputees. In this study, Han pioneered the use of convolutional neural networks to decode sEMG signals from electrode arrays, enabling more intuitive and precise control of bionic manipulators. By integrating signal processing with deep learning, he advanced the development of intelligent neuroprostheses that respond to natural muscle activity, bridging the gap between biological intent and robotic action. Han’s research has significantly influenced the design of adaptive, real-time control systems for assistive robotics, with implications for rehabilitation engineering and wearable exoskeletons. His work continues to inspire innovations in non-invasive neural interfaces, making him a notable figure in the intersection of biomedical engineering and artificial intelligence.
Research Focus
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Top Papers
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