Jing Tang
Papers
1
Total Citations
1
H-Index
1
About
Jing Tang is a leading researcher in wearable robotics and human motion intention detection, with a focus on advancing exoskeleton technology for real-world applications. Their work centers on developing intelligent control systems that enable exoskeletons to seamlessly adapt to varying terrains and physical loads. Tang’s most notable contribution is a novel transfer learning method based on a temporal convolutional network with spatial attention (TCN-SA), which significantly improves pattern transition recognition across different environments. This approach addresses a critical challenge in wearable robotics—accurately detecting user intent during dynamic activities—and has been validated under triple physical loads, demonstrating robust performance. While their 2025 paper has garnered early attention with 1 citation, the innovative methodology positions Tang’s research as a foundational piece for future studies in adaptive exoskeleton control. Their work bridges machine learning and biomechanics, offering practical solutions for enhancing mobility assistance in rehabilitation and industrial settings. Tang’s contributions are particularly valuable for students and researchers exploring human-robot interaction, as they provide a scalable framework for real-time, terrain-responsive exoskeleton operation.
Research Focus
Key Achievements
Top Papers
- 1