Yixuan Tong

NYU Langone Health, Shenyang Institute of Automation, State Key Laboratory of Robotics

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Total Citations

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About

Yixuan Tong is a researcher at the intersection of robotic surgery, brain-computer interfaces (BCIs), and computer vision. Their work focuses on enhancing human-robot interaction and precision in medical and assistive contexts. A key contribution is their study on the learning curve for robot-assisted spine surgery using the ExcelsiusGPS® system, which demonstrated that proficiency in this technology can be achieved after approximately 15 cases, with significant reductions in operative time and complications. This work, published in 2024 and already garnering 14 citations, provides crucial guidance for surgical teams adopting robotic assistance. Tong has also advanced assistive robotics through a BCI-based semi-autonomous system that interprets EEG signals to execute grasping tasks, offering a solution for users with limited hand mobility. Additionally, they developed a fast, straightforward hand-eye calibration method using stereo cameras, simplifying a traditionally complex process for robotic systems. With a growing citation record and contributions spanning clinical and technical domains, Tong’s research is shaping the future of safe, intuitive, and efficient robotic systems in healthcare and beyond.