Zhongzhi Li

Papers

Total Citations

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About

Zhongzhi Li is a researcher at the forefront of intelligent robotics and human-machine interaction, with a primary focus on enhancing machine perception and control through deep learning. His work bridges the gap between biological signals and robotic systems, particularly in the domains of prosthetic control and autonomous navigation. Li’s most cited paper, "Intelligent Classification of Multi-Gesture EMG Signals Based on LSTM" (2020, 11 citations), introduces a novel application of Long Short-Term Memory networks to decode electromyographic signals for precise gesture recognition, directly advancing the field of rehabilitation robotics and artificial limb control. Building on this, his second highly cited work, "Robot Ground Classification and Recognition Based on CNN-LSTM Model" (2021, 7 citations), proposes a hybrid CNN-LSTM architecture that enables mobile robots to accurately classify terrain from historical sensor data, improving autonomous navigation in complex environments. Through these contributions, Li demonstrates a consistent ability to integrate temporal and spatial feature learning, achieving practical improvements in robotic adaptability and user intent prediction. His research not only pushes the boundaries of intelligent control systems but also holds significant promise for real-world applications in assistive technology and field robotics.