Kairui Guo
Papers
2
Total Citations
22
H-Index
2
About
Kairui Guo is a researcher at the forefront of assistive rehabilitation robotics, specializing in human–machine interaction and adaptive control systems. His work centers on developing intelligent frameworks that enable robots to respond intuitively to human physiological signals, particularly for stroke and mobility-impaired patients. A key contribution is his pioneering use of surface electromyography (S-EMG) signals to estimate ankle joint torque and angle, a method that allows rehabilitation robots to provide precise, real-time assistance tailored to a patient’s muscle activity. This approach, detailed in his 2019 paper with 17 citations, has laid groundwork for more responsive exoskeletons and prosthetics. More recently, Guo introduced a Cooperative Markov Decision Process model for human–machine co-adaptation, published in 2024. This novel framework addresses the critical challenge of mutual learning between patient and robot, enabling dynamic adjustment of therapy as the user’s motor control improves. Though early in its citation impact, this work represents a significant conceptual advance in rehabilitation robotics. Guo’s research bridges signal processing, control theory, and clinical application, offering tangible pathways toward more effective, personalized robotic therapy.
Research Focus
Key Achievements
Top Papers
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