Mohammed Hicham Zaggaf
École Normale Supérieure de l'Enseignement Technique de Mohammedia
Papers
1
Total Citations
3
H-Index
1
About
Mohammed Hicham Zaggaf’s research lies at the intersection of human-robot interaction, computer vision, and intelligent reasoning systems. His most cited work, “Structured Human-Head Pose Representation for Estimation Using Fuzzy Lattice Reasoning (FLR)” (2020), introduces a novel framework for estimating head pose—a critical modality for enabling social robots to interpret human attention and intent during interaction. By leveraging fuzzy lattice reasoning, Zaggaf’s approach enhances the robustness and accuracy of pose estimation under real-world conditions, addressing key challenges in vision-based human-robot communication. This contribution has garnered 3 citations, reflecting its emerging relevance in the field. Zaggaf’s work is notable for bridging theoretical reasoning models with practical robotic applications, offering a structured representation that improves both efficiency and effectiveness. His research underscores a commitment to advancing intuitive, non-verbal interaction channels between humans and machines, with potential implications for assistive robotics, autonomous systems, and affective computing. As a researcher, Zaggaf continues to explore how fuzzy logic and lattice-based methods can refine perceptual capabilities in social robots, laying groundwork for more responsive and adaptive human-robot collaboration.
Research Focus
Key Achievements
Top Papers
- 1