Mouaz Al Kouzbary
Papers
5
Total Citations
31
H-Index
4
About
Mouaz Al Kouzbary is a researcher at the forefront of rehabilitation robotics, specializing in the development of intelligent, adaptive control systems for powered lower-limb prostheses. His work directly addresses the critical challenge of restoring natural, energy-efficient gait for individuals with transfemoral and transtibial amputations. Al Kouzbary’s major contributions lie in leveraging nonlinear neural networks, specifically Nonlinear Autoregressive networks with Exogenous Inputs (NARX), to generate robust and adaptive walking patterns for prosthetic ankles and feet. His most cited work (14 citations) demonstrates how these networks can emulate intact limb behavior across varying speeds and terrains. He has also advanced mechanical design, optimizing a robotic knee prosthesis with a cycloidal gear and four-bar mechanism using a Particle Swarm Algorithm to reduce the 60% extra metabolic cost typically faced by users. Further expanding his impact, Al Kouzbary has pioneered sensorless control systems for assistive robotic ankles and applied nonlinear dynamics tools to analyze human ambulation as a chaotic time-series, offering novel insights for prosthesis control. His research portfolio, spanning from algorithmic estimation of body segmental orientation to mechanical optimization, marks him as a key innovator in creating more responsive and clinically viable prosthetic technologies.
Research Focus
Key Achievements
Top Papers
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- 4Sensorless control system for assistive robotic ankle-foot4 citations · 2018
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