Papers
5
Total Citations
135
H-Index
5
About
Tao Zhen is a prominent researcher specializing in human gait analysis, deep learning-based motion recognition, and rehabilitation robotics. His work sits at a compelling intersection of biomedical engineering and artificial intelligence, focusing on developing sophisticated algorithms to accurately detect and classify walking gait phases using inertial sensor data. Zhen's most significant contribution is his pioneering application of advanced neural network architectures to gait phase detection. His 2019 paper employing an LSTM-DNN algorithm — his most cited work with 64 citations — established a strong foundation for using acceleration signals in biometric gait analysis, with direct applications in exoskeleton-assisted robots and disease diagnosis. He further refined this approach through ensemble methods, introducing a Voting-Weighted Integrated Neural Network in 2020 and exploring hybrid deep-learning frameworks incorporating Gaussian fusion of spatiotemporal networks. Collectively accumulating over 135 citations, Zhen's body of work has meaningfully advanced real-world rehabilitation technology, culminating in his 2023 research on real-time exoskeleton locomotion trajectory control through multi-modal fusion. His research consistently bridges theoretical machine learning innovation with practical clinical and robotic applications, making him a valuable voice in the growing field of intelligent rehabilitation systems.
Research Focus
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