Xiang Bao
Papers
1
Total Citations
7
H-Index
1
About
Xiang Bao is a researcher at the forefront of wearable robotics and human-robot interaction, with a primary focus on intelligent locomotion mode recognition for lower limb exoskeletons. His most notable contribution is the development of a SE-DenseNet-LSTM hybrid model, which integrates a dense convolutional network with long short-term memory and a squeeze-and-excitation attention mechanism. This innovative architecture significantly enhances the accuracy and robustness of real-time gait phase detection, a critical capability for achieving seamless, flexible control in powered exoskeletons. By enabling the system to better interpret human intent from biomechanical signals, Bao’s work directly addresses a fundamental challenge in assistive robotics. His 2024 paper on this model has already garnered 7 citations, reflecting its immediate relevance and impact in the field. Bao’s research sits at the intersection of deep learning, sensor fusion, and rehabilitation engineering, offering practical pathways toward more adaptive and responsive wearable devices. His contributions are particularly valuable for students and engineers working on next-generation prosthetics and exoskeleton control systems.
Research Focus
Key Achievements
Top Papers
- 1