Shangjie Tang
Papers
2
Total Citations
45
H-Index
2
About
Shangjie Tang is a researcher advancing the field of rehabilitation robotics and human motion analysis, with a core focus on kinematic and muscle synergies for assistive device control. Their major contributions lie in deciphering how the human upper limb coordinates multi-degree-of-freedom movements and translating these biological principles into intelligent exoskeleton motion planning. Tang’s most-cited work (2019, 41 citations) pioneered the use of kinematic synergy analysis to generate spatiotemporal motion patterns that naturally match human joint characteristics, addressing a critical gap in rehabilitation exoskeleton control. This research provides a foundational framework for more intuitive, human-like robotic assistance. Additionally, Tang has explored muscle synergy-based strategies, using surface EMG signals to design dynamic power-assisted devices that adapt to user intent. By bridging neural activation patterns with mechanical support, their work enables more responsive and personalized rehabilitation and assistive technologies. Tang’s research is particularly impactful for students and engineers developing next-generation wearable robots, offering a principled approach to creating devices that move with, rather than against, the user’s natural biomechanics.
Research Focus
Key Achievements
Top Papers
- 1
- 2Research on Power-Assisted Strategy and Device Based on Muscle Synergy4 citations · 2019