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About

Ahmed Soliman is a rising researcher in biomechatronics and human motion analysis, with a focus on advancing lower-limb rehabilitation and prosthetic technologies. His work centers on three key areas: gait phase estimation, robotic prosthesis design, and real-time state estimation for wearable systems. In his most-cited paper (2022, 7 citations), Soliman developed a linear regression model using inertial measurement units (IMUs) to estimate gait phases during unsupervised outdoor walking, enabling practical, real-world applications for injury risk tracking and rehabilitation robotics. He also designed a novel mobile 3-RPR parallel manipulator for a lower-leg gait emulator (2022, 5 citations), providing a testing platform that replicates realistic gait conditions to accelerate prosthetic development. Additionally, Soliman introduced an Error-State Kalman Filter (2022, 4 citations) for online ankle angle estimation in a 2-DOF robotic prosthetic ankle, using low-cost sensors to track inversion-eversion, external-internal, and dorsiflexion-plantarflexion movements. His contributions bridge the gap between laboratory-controlled experiments and unsupervised, outdoor environments, offering scalable solutions for assistive devices. With a growing citation record and innovative designs, Soliman is establishing himself as a key contributor to wearable robotics and human motion analysis.