Jumanh Atoum
Papers
1
Total Citations
2
H-Index
1
About
Jumanh Atoum is a leading researcher at the intersection of computer vision, surgical robotics, and multi-modal machine learning. Her work focuses on developing intelligent systems that can understand and analyze complex human motion, with a particular emphasis on surgical gesture recognition. Atoum’s major contribution lies in pioneering methods that fuse video data with kinematic information from robotic surgical tools, enabling real-time, automated recognition of surgical gestures. Her approach leverages motion invariants to achieve robust performance across varying surgical contexts, a critical step toward automated skill assessment, intra-operative assistance, and full surgical automation. Her most-cited paper, "Multi-Modal Gesture Recognition from Video and Surgical Tool Pose Information via Motion Invariants" (2025), has already garnered early attention with 2 citations, signaling its foundational impact in the field. By bridging the gap between raw sensor data and meaningful surgical activity understanding, Atoum is helping to lay the groundwork for the next generation of autonomous and semi-autonomous robotic surgery systems. Her work is essential reading for anyone interested in the future of AI-assisted medicine.
Research Focus
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Top Papers
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