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About

Samer A. Mohamed is a robotics and artificial intelligence researcher whose work sits at the intersection of humanoid robotics, human motion recognition, and intelligent control systems. His research focuses on three interconnected domains: human activity recognition (HAR) for wearable devices, bipedal robot design and locomotion control, and gait phase classification for assistive technologies. Mohamed's most impactful contribution is a lightweight artificial neural network for recognizing activities of daily living, designed specifically for low-cost wearable devices — a paper that has already garnered 11 citations since 2023, reflecting its practical relevance to assistive robotics. His earlier work on designing and controlling a 12-degree-of-freedom humanoid lower limb (2018) established his foundation in biomechanically inspired robot design, while his application of Model Predictive Control to NAO humanoid walking (2020) demonstrated his fluency in real-time optimization strategies. More recently, Mohamed has pioneered hybrid Bayesian-heuristic inference frameworks for gait phase recognition, advancing the reliability of phase-based assistive device control. Across his publications, his research consistently bridges computational intelligence and physical robotics, aiming to make assistive technologies more accurate, portable, and practically deployable for supporting human mobility.