Xiaoguang Liu
Papers
4
Total Citations
42
H-Index
3
About
Xiaoguang Liu is a leading researcher in wearable robotics and human-machine interaction, with a focus on developing intelligent assistive devices that enhance human mobility and dexterity. His work spans exoskeleton optimization, prosthetic control, and robotic automation. Liu’s most impactful contribution is in human-in-the-loop optimization for portable hip exoskeletons, where his 2023 paper (24 citations) demonstrates a novel approach to reducing muscle activity during walking by customizing assistance in real time—a breakthrough that cuts lengthy calibration periods and improves practical usability. He has also advanced upper-limb rehabilitation and prosthetic control using deep learning architectures like SE-TCN and improved Temporal Convolutional Networks (TCN), enabling continuous joint angle estimation and real-time control of intelligent prosthetic hands (9 and 6 citations, respectively). His earlier work on wall-climbing robots for automatic welding (3 citations) showcases his versatility in robotics. With a growing citation record and a clear trajectory toward personalized, efficient wearable systems, Liu is shaping the future of assistive robotics, making his research essential for students and engineers in rehabilitation engineering and human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1
- 2SE-TCN network for continuous estimation of upper limb joint angles9 citations · 2022
- 3
- 4Control System of a Wall Climbing Robot for Automatic Welding3 citations · 2018