Leilei Wang
Papers
2
Total Citations
6
H-Index
2
About
Leilei Wang is a rising researcher in the field of rehabilitation robotics and dynamic systems, with a focus on human–robot interaction and motion control. Her work centers on two key areas: predicting human movement intentions for lower limb exoskeletons and modeling the complex dynamics of rigid–flexible coupled parallel robots. In her most-cited paper (2024, 4 citations), Wang developed a novel neural network architecture combining temporal convolutional networks and bidirectional LSTMs with quantile regression to accurately predict lower limb joint angles from multistream signals—a critical step toward responsive rehabilitation exoskeletons for patients with spinal cord injuries or strokes. Her second major contribution (2025, 2 citations) addresses the challenge of vibration and elastic deformation in flexible robotic members by proposing a dynamic modeling method that improves motion accuracy and performance. While her citation counts are still growing, Wang’s work is notable for bridging advanced machine learning with practical rehabilitation needs, offering promising solutions for assistive robotics. Her research is particularly relevant for students and engineers interested in the intersection of biomechanics, control systems, and neural networks for medical applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2