Shi-Chang Zhang
Papers
2
Total Citations
16
H-Index
2
About
Shi-Chang Zhang is a pioneering researcher at the intersection of robotic prosthetics and hardware acceleration, specializing in the development of intelligent control systems for lower-limb assistive devices. His work focuses on enhancing the computational efficiency of locomotion mode recognition for robotic transtibial prostheses, particularly through the innovative use of field-programmable gate arrays (FPGAs) and system-on-chip (SoC) architectures. Zhang’s major contributions include the design and implementation of on-board hardware acceleration systems that enable real-time support vector machine (SVM) training and classification, significantly improving the responsiveness and autonomy of prosthetic limbs. His most-cited papers, each garnering 8 citations, demonstrate the foundational impact of his work: one analyzes the performance of hardware-accelerated locomotion recognition, while the other presents a complete SoC-FPGA-based acceleration system for on-board SVM model training. These studies are notable for bridging the gap between advanced machine learning algorithms and practical, embedded hardware solutions, paving the way for more adaptive and intelligent prosthetic devices. Zhang’s research holds promise for transforming rehabilitation engineering, offering tangible benefits to amputees through faster, more reliable prosthetic control.
Research Focus
Key Achievements
Top Papers
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- 2