Papers
6
Total Citations
141
H-Index
4
About
Linglong Li is a leading researcher in the field of rehabilitation robotics, with a primary focus on lower limb exoskeletons and human–robot interaction. Her work centers on solving the critical challenge of accurately recognizing human motion intention—a prerequisite for achieving natural, compliant, and safe control in assistive devices. Li’s major contributions include comprehensive reviews that synthesize the state of the art in motion intention recognition and interactive control for exoskeletons, which have garnered significant attention (57 and 50 citations, respectively). She has also developed novel control methods, such as a self-adaptive sliding mode controller for rehabilitation exoskeletons, and advanced machine learning approaches, including domain-adaptive convolutional neural networks for gait phase recognition under varying speeds. Her research extends to safe movement planning using Dynamic Movement Primitives and Control Barrier Functions, as well as innovative work on flexible webbed wings for wave gliders. With over 140 total citations, Li’s work is shaping the future of intelligent, adaptive rehabilitation technologies, bridging the gap between theoretical control and practical, patient-centered robotic assistance.
Research Focus
Key Achievements
Top Papers
- 1Human Lower Limb Motion Intention Recognition for Exoskeletons: A Review57 citations · 2023
- 2Interactive Control of Lower Limb Exoskeleton Robots: A Review50 citations · 2024
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