Nianyin Zeng
Papers
6
Total Citations
240
H-Index
6
About
Nianyin Zeng is a leading researcher at the intersection of intelligent robotics, rehabilitation engineering, and human–robot interaction. His work centers on developing advanced computational methods for robot path planning, human motion prediction, and neural signal decoding—critical components for next-generation assistive and autonomous systems. Zeng’s most influential contribution is a path planning algorithm for intelligent robots based on a switching local evolutionary particle swarm optimization (PSO) approach, which has garnered 90 citations for its novel integration of non-homogeneous Markov chains and differential evolution. He has also pioneered the use of artificial neural networks for intelligent prediction of human lower extremity joint moments (58 citations), enabling quantitative rehabilitation assessment without specialized equipment. More recently, his fusion of improved A* algorithms with segmented Bézier curves (45 citations) has advanced mobile robot navigation by reducing path-turning points and search time. Zeng’s work on discrete hand motion intention decoding from transient myoelectric signals and recurrent-neural-network-based noise resistance models further demonstrates his commitment to making human–robot interaction more seamless and robust. His research continues to shape the future of intelligent robotics and personalized rehabilitation technology.
Research Focus
Key Achievements
Top Papers
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