Mohamed Waleed Fakhr
Arab Academy for Science, Technology, and Maritime Transport
Papers
1
Total Citations
2
H-Index
1
About
Mohamed Waleed Fakhr is a researcher at the forefront of human-robot interaction, with a particular focus on enabling collaborative robots to understand and respond to human motion. His key research areas include multimodal deep learning, human-robot handover classification, and assistive robotics. Fakhr’s most notable contribution is his work on developing a multimodal deep learning model for classifying human handover motions—a critical capability for robots that must safely and intuitively exchange objects with people. By addressing the gap in classifying different types of handover gestures, his research directly advances the fluidity and safety of human-robot collaboration. While his most-cited paper (2022) has garnered early recognition with 2 citations, it represents foundational work in a rapidly growing field. Fakhr’s achievements are particularly significant for students and researchers interested in the intersection of computer vision, robotics, and deep learning. His work not only pushes the boundaries of how robots perceive human intent but also lays the groundwork for more natural, adaptive robotic assistants in industrial and domestic settings.
Research Focus
Key Achievements
Top Papers
- 1Multimodal deep learning model for human handover classification2 citations · 2022