Binghui Fan

Shandong University of Science and Technology

Papers

Total Citations

H-Index

About

Binghui Fan’s research lies at the intersection of medical robotics and neural control, with key contributions in inverse kinematics for surgical manipulators and brain-computer interfaces for prosthetic limbs. His most cited work (2021, 24 citations) introduces an improved weighted gradient projection method that solves the inverse kinematics problem for redundant surgical manipulators, achieving high pose accuracy essential for delicate operations—a critical advance over traditional approaches. This work addresses the unique challenge of maintaining end-effector precision in constrained surgical environments. Earlier, Fan developed an online control system for upper limb prostheses driven by motor imagery EEG signals (2011, 16 citations), combining common spatial pattern feature extraction with probabilistic neural network classification across six imagined tasks. This demonstrated real-time, non-invasive control of artificial limbs, bridging neural signals with mechanical actuation. Additionally, his analysis of singular configurations in robotic manipulators (2021, 11 citations) provides methods for identifying problematic poses in serial robots that do not satisfy the Pieper criterion, enhancing robotic reliability. With over 50 total citations, Fan’s work directly impacts surgical robotics and assistive technology, offering practical solutions for high-stakes applications where precision and responsiveness are paramount.