Papers
53
Total Citations
636
H-Index
15
About
Yuquan Leng is a robotics and human-robot interaction researcher whose work spans medical robotics, rehabilitation engineering, and intelligent perception systems. With over 314 citations across his most prominent publications, Leng has established himself as a versatile contributor to several cutting-edge domains within robotics research. His most recognized contribution, "Linked Dynamic Graph CNN" (54 citations), advances 3D point cloud processing for environmental understanding—a foundational capability for autonomous robots. Equally impactful is his work in rehabilitation robotics, where he has developed intelligent assessment and gait correction methods for stroke patients using lower limb exoskeleton robots, collectively garnering over 74 citations and offering clinically meaningful improvements over manual evaluation techniques. Leng has also made notable strides in medical imaging robotics, designing autonomous ultrasound scanning systems for both breast and lung diagnostics—the latter targeting COVID-19 screening—demonstrating his ability to translate robotic solutions into urgent healthcare contexts. His research further extends to assistive technologies, including powered prostheses, supernumerary robotic limbs, and wearable load-bearing systems. Together, these contributions reflect a coherent vision: creating intelligent, autonomous robotic systems that meaningfully augment human capability in both clinical and real-world environments.
Research Focus
Key Achievements
Top Papers
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- 3A Flexible and Fully Autonomous Breast Ultrasound Scanning System37 citations · 2022
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