Alexandros Agapitos
Papers
2
Total Citations
26
H-Index
2
About
Alexandros Agapitos is a researcher whose work sits at the intersection of robotics, machine learning, and human activity recognition. His primary research areas include ubiquitous robotics, evolutionary computation (particularly Genetic Programming), and dynamic neural networks for time-series analysis. A major contribution is his pioneering comparison of dynamic Artificial Neural Networks (ANNs) and Genetic Programming for physical human action recognition, demonstrating how mobile robots can classify human activities from sensor data in real-world, indoor environments. His 2008 paper on this topic, which has garnered 24 citations, remains a foundational reference for researchers exploring evolutionary approaches to robotics. Agapitos also investigated the use of mechanical feature attributes—such as forces and linear/non-linear classifiers—to model and distinguish physical activities with greater clarity. While his citation counts reflect a focused, early-career impact, his work is notable for bridging the gap between evolutionary algorithms and practical robotic perception, offering a rigorous, data-driven methodology for action recognition that continues to inform modern human-robot interaction studies.
Research Focus
Key Achievements
Top Papers
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