Papers
2
Total Citations
30
H-Index
2
About
Jiewen Tan is a rising researcher at the forefront of medical robotics, specializing in the modeling, localization, and control of magnetically-actuated systems for minimally-invasive surgery. Their work addresses critical challenges in wireless capsule endoscopy and robotic catheterization, where precise, remote control is essential. Tan’s most cited paper (2021, 28 citations) introduces a novel kinematic model for magnetically-actuated robotic catheters (MARC), accounting for the complex, nonlinear coupling of gravitational, magnetic, and elastic forces when driven by external permanent magnets—a foundational contribution that enhances the predictability and safety of these devices. More recently, Tan developed MobilePosenet (2025), a lightweight neural network for calibration-free, 5-DOF permanent magnet localization. This data-driven approach overcomes the computational delays and sensitivity issues of traditional dipole-model algorithms, offering a practical, real-time solution for tracking wireless capsule endoscopes. By bridging theoretical modeling with efficient, deployable AI, Tan’s work is paving the way for more autonomous, accurate, and accessible robotic surgical tools. Their dual focus on fundamental physics and applied machine learning marks them as a versatile innovator in the field.
Research Focus
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Top Papers
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