Xiaoqiao Huang
Papers
1
Total Citations
2
H-Index
1
About
Xiaoqiao Huang is a researcher specializing in medical robotics and surgical automation, with a particular focus on enhancing the precision and safety of robot-assisted percutaneous procedures. Their most-cited work, "Dynamic Force Modeling for Robot-Assisted Percutaneous Operation Using Intraoperative Data" (2017), introduces a novel approach to modeling dynamic forces during needle insertion by leveraging real-time intraoperative data. This contribution is critical for improving haptic feedback and control in robotic surgery, reducing tissue damage and increasing procedural accuracy. Though early in its citation impact, this foundational study has laid important groundwork for adaptive force control in minimally invasive interventions. Huang’s research bridges the gap between theoretical modeling and clinical application, offering practical solutions for real-time surgical decision-making. Their work is particularly valuable for students and engineers developing next-generation surgical robots, as it demonstrates how data-driven models can enhance the safety and efficacy of robot-assisted operations. Huang’s dedication to advancing intraoperative data integration marks them as an emerging voice in the field of medical robotics.
Research Focus
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Top Papers
- 1