Tie Liang

Papers

Total Citations

H-Index

About

Tie Liang is a researcher at the forefront of human-computer interaction and rehabilitation robotics, specializing in the use of surface electromyographic (sEMG) signals for advanced prosthetic and exoskeleton control. Their key research areas include real-time motion estimation, neural signal processing, and intelligent robotic systems for upper limb rehabilitation. Liang’s major contributions include the development of the SE-TCN network, a novel architecture that enables continuous and accurate estimation of upper limb joint angles from sEMG signals—a critical step toward natural, intuitive control of assistive devices. This work, published in 2022, has already garnered 9 citations, reflecting its growing influence in the field. Additionally, Liang pioneered a real-time control system for intelligent prosthetic hands using an improved Temporal Convolutional Network (TCN), achieving 6 citations for its practical advancements in human-robot interaction. By bridging the gap between biological signals and robotic actuation, Liang’s research directly enhances the quality of life for individuals with limb loss or motor impairments. Their work stands out for its focus on real-time, robust control, making it highly relevant for both clinical rehabilitation and next-generation assistive technologies.