Papers
13
Total Citations
122
H-Index
7
About
Guokun Zuo is a robotics and rehabilitation engineering researcher whose work sits at the intersection of human-robot interaction, intelligent control systems, and motor recovery therapies. His scholarship focuses primarily on developing advanced control strategies for upper-limb rehabilitation robots, with particular emphasis on making robotic assistance adaptive, patient-responsive, and therapeutically effective for stroke survivors and individuals with motor dysfunction. Among his most significant contributions is pioneering assist-as-needed control frameworks that dynamically modulate robotic assistance based on a patient's real-time rehabilitation needs, drawing on techniques such as Gaussian Mixture Models and fuzzy adaptive control—work that has garnered over 27 and 19 citations respectively. His 2019 research on sensorless force estimation using disturbance observers addressed a critical challenge in human-robot interaction: detecting patient motion intent without dedicated force sensors, earning 15 citations. Zuo has also explored reward-punishment feedback mechanisms to sustain patient engagement during training, and more recently extended his research into brain-computer interfaces and machine vision-based haptic recognition. Collectively, his publications reflect a commitment to building smarter, more empathetic rehabilitation systems that meaningfully improve patient outcomes and reduce reliance on human therapists.
Research Focus
Key Achievements
Top Papers
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- 4Dual-Modal Hybrid Control for an Upper-Limb Rehabilitation Robot14 citations · 2022
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