Yarden Sharon
Papers
7
Total Citations
104
H-Index
5
About
Yarden Sharon’s research lies at the intersection of robotic-assisted minimally invasive surgery (RAMIS), human motor control, and surgical skill assessment. Her work systematically characterizes how surgeons move—and how those movements can be taught, evaluated, and improved through technology. A central contribution is her discovery of a novel power law linking tool-tip orientation speed to movement geometry during teleoperation, extending classic motor invariants into the surgical domain. She also developed instrument orientation-based metrics for objective skill evaluation, moving beyond simple motion tracking to capture the nuanced spatiotemporal patterns that distinguish expert from novice performance. Her studies on force feedback in robot-assisted needle driving have informed the design of more intuitive haptic interfaces, while her work on data augmentation for deep learning segmentation has improved the robustness of automated surgical analysis. With over 100 citations across her most-cited papers, Sharon’s research has shaped how the field thinks about training protocols, system design, and the fundamental laws governing teleoperated movement. Her findings offer practical pathways toward safer, more effective surgical robots and data-driven curricula for the next generation of surgeons.
Research Focus
Key Achievements
Top Papers
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- 5What Can Spatiotemporal Characteristics of Movements in RAMIS Tell Us?6 citations · 2017
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