Yixiong Zhang

Papers

Total Citations

H-Index

About

Yixiong Zhang’s research lies at the intersection of human-computer interaction and robotics, with a focus on hand gesture recognition and tracking. His most-cited work, “2D Hand Tracking Based on Flocking with Obstacle Avoidance” (2014), addresses a critical challenge in gesture-based interfaces: robustly distinguishing hand movements from skin-colored objects, particularly the face. By applying a flocking algorithm—inspired by collective animal behavior—combined with obstacle avoidance, Zhang’s method enables reliable hand tracking even in cluttered visual environments. This contribution has garnered 2 citations and offers a practical solution for improving natural interaction in both human-computer and human-robot systems. Zhang’s work is notable for its creative cross-disciplinary approach, merging concepts from swarm intelligence with interface design. While his citation count is modest, the novelty of his approach—treating hand tracking as a dynamic, obstacle-aware system—demonstrates his ability to tackle persistent usability issues. For students and researchers exploring gesture-based control, Zhang’s research provides a foundational example of how bio-inspired algorithms can enhance real-world interaction, paving the way for more intuitive and resilient interfaces in robotics and beyond.