Ziyue Dang
Papers
1
Total Citations
10
H-Index
1
About
Ziyue Dang is a researcher advancing the integration of digital twin technology and human-robot interaction for medical training and diagnostics. Their primary research areas include digital twin-based skill training, hands-on user interaction devices, and robotic teleoperation in ultrasonography. Dang's most notable contribution is the development of a digital twin framework combined with a hands-on interaction device to assist in manual and robotic ultrasound scanning, addressing critical gaps in physician training and diagnostic accuracy. This work, published in 2022, has already garnered 10 citations, reflecting its growing impact in the field of medical robotics and simulation. By enabling more efficient and realistic training processes, Dang's research directly tackles the challenge of insufficient ultrasound scanning skills, which can compromise diagnostic reliability. Their innovative approach bridges virtual and physical systems, offering a scalable solution for teleconsultation and remote robotic teleoperation. Dang's work is particularly relevant for students and researchers interested in the convergence of digital twins, haptic interfaces, and medical robotics, demonstrating how immersive training tools can enhance clinical outcomes and expand access to expert-guided procedures.
Research Focus
Key Achievements
Top Papers
- 1