Yi-Lian Chen
Papers
2
Total Citations
14
H-Index
2
About
Yi-Lian Chen is a leading researcher in rehabilitation robotics, with a primary focus on developing intelligent control systems for upper-limb exoskeleton robots. Her work addresses critical challenges in human-robot interaction, particularly in creating adaptive, sensorless control strategies that enhance rehabilitation outcomes. Chen's major contributions include pioneering a velocity field-based active-assistive control framework that overcomes the limitations of conventional time-dependent trajectory methods, enabling more natural and task-based multi-joint rehabilitation exercises. She also developed a sensorless control scheme integrating friction and human intention estimation using Kalman filter-based interactive torque observers, allowing for active-mode therapy without reliance on external sensors. Her most cited paper (2020, 8 citations) and subsequent work (2019, 6 citations) have laid foundational groundwork for more intuitive, patient-responsive robotic rehabilitation. Chen's innovative approaches—particularly her emphasis on eliminating prior trajectory constraints and incorporating real-time human intention estimation—represent significant advances in making exoskeleton therapy more effective and user-centered. Her research continues to shape the future of assistive robotics for clinical rehabilitation.
Research Focus
Key Achievements
Top Papers
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- 2