Papers
18
Total Citations
259
H-Index
8
About
Shouqi Chen is a robotics researcher whose work spans two compelling domains: rehabilitation robotics and bio-inspired climbing robots. His most influential contribution, a multi-mode rehabilitation robot employing magnetorheological actuators and human motion intention estimation (2019, 72 citations), established him as a significant voice in assistive technology for lower extremity paralysis recovery. Building on this foundation, Chen advanced robotic mirror therapy through a reinforcement learning framework for hemiparesis rehabilitation (2020, 37 citations), demonstrating his commitment to intelligent, adaptive systems that respond to individual patient needs. Alongside his rehabilitation work, Chen has made notable strides in wall-climbing robotics, developing bio-inspired systems capable of traversing both rough and smooth surfaces using innovative spine wheels, adhesive belts, and eddy suction mechanisms — research that has collectively garnered over 95 citations across multiple publications. His interdisciplinary approach integrates biomimicry, mechanical design, and advanced control strategies, including RBF neural network-based trajectory tracking for rehabilitation applications. With a focused body of work produced largely between 2019 and 2020, Chen demonstrates a productive research trajectory bridging human-centered robotics and versatile autonomous systems, making his work valuable reading for students in medical robotics, exoskeleton design, and biomimetic engineering.
Research Focus
Key Achievements
Top Papers
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- 2A Multi-Channel Reinforcement Learning Framework for Robotic Mirror Therapy37 citations · 2020
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- 8Robotic mirror therapy system for lower limb rehabilitation8 citations · 2020
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