Bassant M. Elbagoury
Papers
5
Total Citations
22
H-Index
3
About
Bassant M. Elbagoury is a researcher specializing in intelligent control systems, autonomous robotics, and machine learning applications across rehabilitation engineering, humanoid robotics, and autonomous vehicle safety. With a career spanning over a decade, Elbagoury has consistently tackled complex real-time control challenges where traditional approaches fall short. Among their most notable contributions is a hybrid rehabilitation robot control system that ingeniously combines Kalman filtering, support vector machines, and particle swarm optimization to interpret EMG muscle signals in real time — a breakthrough approach for stroke patient rehabilitation that has garnered 8 citations. Elbagoury has also made significant strides in multi-agent humanoid robot control, developing Extended Case-Based Behavior systems for competitive RoboCup environments, demonstrating expertise in dynamic, multi-robot coordination across several publications dating back to 2009. More recently, Elbagoury extended their intelligent systems research into autonomous vehicle safety, pioneering LiDAR-integrated deep learning engines for pre-crash detection and hybrid A* path planning for collision avoidance. Collectively accumulating over 20 citations, their body of work reflects a versatile and applied research vision — bridging artificial intelligence, sensor fusion, and real-world robotics to address critical challenges in healthcare, sport robotics, and transportation safety.
Research Focus
Key Achievements
Top Papers
- 1
- 2Extended Case-Based Behavior Control for Multi-Humanoid Robots7 citations · 2015
- 3
- 4Hierarchical Case-Based Reasoning Behavior Control for Humanoid Robot2 citations · 2009
- 5