Liming Shu

The University of Tokyo, Dalian University of Technology

Papers

Total Citations

H-Index

About

Liming Shu is a rising leader in the field of surgical robotics and neural-controlled prosthetics, whose work bridges the gap between biomechanics, signal processing, and machine learning. His primary research areas include intelligent surgical monitoring, neural decoding for prosthetic control, and personalized exoskeleton assistance. Shu’s major contribution lies in developing non-invasive, real-time sensing methods to enhance safety and precision in medical devices. His most-cited work, "Bone Milling: On Monitoring Cutting State and Force Using Sound Signals" (2022, 20 citations), introduces a novel acoustic-based approach to detect cutting conditions during orthopedic surgery, potentially reducing tissue damage and surgical time. This work has been recognized for its practical impact on improving intraoperative safety. In parallel, Shu is advancing dexterous prosthesis control through EMG decomposition, decoding neural information to restore natural hand function in amputees. His recent hybrid approach integrating musculoskeletal simulation with machine learning (2024) offers personalized ankle-foot exoskeleton strategies, addressing individual variability in gait assistance. With a growing citation record and a focus on translating complex biomechanical signals into actionable clinical tools, Shu is shaping the future of intelligent, human-centered medical technologies.