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About

Zijun Wei is a leading researcher in the field of rehabilitation robotics and human motion intention prediction, with a primary focus on restoring upper-limb function for post-stroke patients. His work centers on the critical challenge of using surface electromyography (sEMG) signals to decode and anticipate human movement, enabling more responsive and effective robotic rehabilitation. Wei’s major contributions include pioneering systematic reviews and novel feature extraction methods that bridge the gap between raw biological signals and precise robotic control. His highly cited 2024 review, with 53 citations, provides a comprehensive taxonomy of model-based and model-free approaches for continuous motion intention prediction, establishing a foundational roadmap for the field. In parallel, his innovative work on muscle synergy features—extracting neural coordination patterns from sEMG—has demonstrated a powerful new way to predict wrist joint kinematics with greater accuracy. This approach, detailed in his 2024 paper, offers a more physiologically grounded method for tailoring robotic assistance to individual patient needs. By advancing both the theoretical framework and practical algorithms for sEMG-driven control, Wei is directly shaping the next generation of intelligent, adaptive rehabilitation technologies that promise to significantly improve recovery outcomes for stroke survivors.