Yuding Cui

Huazhong University of Science and Technology

Papers

Total Citations

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About

Yuding Cui’s research focuses on the intersection of neural network architectures, biomedical signal processing, and rehabilitation robotics. Their most notable contribution is the development of a dynamic recurrent neural network (DRNN)-based classification scheme for myoelectric control of upper limb rehabilitation robots. This work, published in 2014, addresses critical challenges in decoding human motion intent from electromyographic signals, proposing solutions for network structure selection, data segmentation, and feature extraction using time-domain methods. The approach aims to improve the responsiveness and accuracy of robotic assistive devices for individuals with motor impairments. Additionally, Cui has contributed to signal processing methodology with an anti-aliasing and de-noising hybrid algorithm for wavelet transforms (2013), enhancing the fidelity of biomedical signal analysis. While their citation counts remain modest at 2 per paper, these foundational studies represent early-stage contributions to the growing field of intelligent human-machine interfaces for rehabilitation. The work demonstrates a systematic approach to integrating recurrent neural dynamics with real-time control systems, laying groundwork for future advances in adaptive prosthetic and orthotic technologies.