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About

Ahmed Elsabbagh is a leading researcher in the field of rehabilitation robotics, with a primary focus on the development of intelligent control systems for lower limb exoskeletons. His work centers on solving the critical challenge of seamless human-machine interaction, particularly for patients with movement disorders. Elsabbagh’s major contributions include pioneering a dynamic adaptive neural network algorithm that fuses multi-feature surface electromyography (sEMG) signals, dramatically improving the accuracy of lower limb motion intention recognition—a key bottleneck in exoskeleton control. He has also advanced human-robot interaction by designing a novel fuzzy radial-based impedance controller (RBF-FVI) that enhances the safety and adaptability of rehabilitation exoskeletons. Demonstrating a comprehensive approach, his research extends to optimizing gait trajectories through a novel beetle swarm optimization algorithm (BSO-EOLLFF), which addresses issues of low accuracy and slow convergence. With his most cited paper garnering 74 citations, Elsabbagh’s work is establishing foundational methods for more responsive, intelligent, and patient-specific robotic rehabilitation systems.