Seung Chan Lee
Papers
1
Total Citations
2
H-Index
1
About
Seung Chan Lee is a researcher advancing the field of wearable robotics and human gait assistance. His work focuses on developing intelligent control systems for hip gait assist robots, particularly through machine learning-based locomotion mode detection. Lee’s major contribution involves the application of Radial Basis Function Support Vector Machines (RBF-SVM) to automatically classify and predict human locomotion modes—such as level walking, stair ascent, and stair descent—enabling more responsive and adaptive robotic support. This approach addresses a critical limitation in universal hip gait assist devices, which often struggle with real-time detection of user intent. While his most-cited paper has garnered 2 citations, its foundational methodology represents an important step toward safer and more intuitive human-robot interaction in rehabilitation and mobility assistance. Lee’s work sits at the intersection of biomechanics, machine learning, and assistive robotics, with potential applications for elderly mobility support and post-stroke gait rehabilitation. His research contributes to the growing effort to make wearable robotic systems more autonomous and context-aware, ultimately aiming to improve quality of life for individuals with gait impairments.
Research Focus
Key Achievements
Top Papers
- 1