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About

Bekzat Amanov is an emerging robotics and biomedical engineering researcher whose work bridges mechanical design, autonomous systems, and rehabilitation technology. His research spans three interconnected domains: walking robot locomotion, exoskeleton control, and medical service robotics — areas where his contributions are already drawing meaningful attention from the scientific community. Amanov's most recognized work focuses on the optimal synthesis of walking robot legs, where he proposed an innovative six-link lambda-type mechanism that prioritizes energy efficiency and control simplicity over conventional bio-inspired designs, earning 6 citations since its 2023 publication. Building on this foundation, he applied non-dominated sorting genetic algorithms to optimize leg linkage for horizontal propulsion, demonstrating a sophisticated command of multi-objective engineering optimization. In parallel, Amanov has made strides in rehabilitation engineering, developing deep learning frameworks — leveraging CNNs and RNNs — to classify EMG signals for lower limb exoskeleton control, a study that has already garnered 5 citations. His work on LiDAR-based hospital service robots further highlights his versatility, combining SLAM navigation with real-world medical applications. Collectively accumulating over 17 citations, Amanov represents a promising voice at the intersection of intelligent robotics and human-centered engineering.