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About

Dianguo Cao is a researcher at the forefront of intelligent rehabilitation robotics, specializing in the seamless integration of human physiology and robotic control. His work centers on two key areas: surface electromyography (sEMG)-driven human-machine interfaces and the advanced control of cable-driven rehabilitation robots. Cao’s major contributions include developing a state-space model that uses sEMG signals to accurately estimate continuous joint angles—a critical step for exoskeletons that can intuitively follow a patient’s intent. He has also pioneered robust control strategies, such as integral sliding mode control combined with nonlinear extended state observers, to ensure stable and safe passive training for cable-driven systems. His research on trajectory planning for lower limb rehabilitation actions addresses a crucial gap by focusing on the practical execution of therapy movements. With a growing body of work that has garnered early citations, Cao is establishing a strong foundation for next-generation rehabilitation technology. His notable achievements include designing novel cable-driven robot architectures and advancing methods for real-time, personalized human-robot interaction, promising more effective and responsive rehabilitation for patients.