Papers
4
Total Citations
23
H-Index
3
About
Yinglun Tan is a pioneering researcher at the intersection of orthopaedic robotics, musculoskeletal modeling, and assistive healthcare technologies. His primary research focuses on developing intelligent robotic systems for fracture reduction surgeries, where he has made significant contributions by integrating biomechanical principles with robotic control. Tan's most influential work introduces a hill-based musculoskeletal model for fracture reduction robots, addressing the critical challenge of excessive reduction forces during surgery—a problem that has hindered clinical adoption of these robots. This foundational paper has garnered 11 citations, establishing a new framework for safer orthopaedic interventions. He further advanced the field by optimizing robot trajectories with musculoskeletal integration features, demonstrating how muscle forces impact surgical planning—a previously underexplored area. Beyond orthopaedics, Tan has innovated in robot pose estimation with an icosahedron marker system that improves upon traditional ArUco methods, and developed impedance iterative learning sliding mode control algorithms for robot-assisted bathing of the elderly. His work bridges surgical precision with compassionate care technologies, earning him recognition as a rising leader in medical robotics.
Research Focus
Key Achievements
Top Papers
- 1Hill‐based musculoskeletal model for a fracture reduction robot11 citations · 2021
- 2
- 3The Icosahedron Marker for Robots 6-Dof Pose Estimation3 citations · 2021
- 4