Zhuo Qi

Papers

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About

Dr. Zhuo Qi is a leading researcher in the field of intelligent exoskeleton robotics, with a primary focus on locomotion mode recognition and sensor optimization. His work addresses critical challenges in human-robot interaction by developing algorithms that enable exoskeletons to accurately interpret and respond to human movement. Dr. Qi’s most cited contribution, the FSM-HSVM-based locomotion mode recognition system (2022, 13 citations), introduces a hierarchical support vector machine algorithm integrated with a finite state machine. This innovation significantly enhances the reliability of gait phase detection by utilizing hip joint angle data, marking a key advancement in real-time exoskeleton control. In his equally impactful work on sensor screening (2021, 12 citations), Dr. Qi proposed an improved greedy reduction algorithm based on a neighborhood rough set model. This method systematically identifies the most effective sensor combinations for gait recognition, reducing system complexity while maintaining high classification accuracy. Together, these contributions demonstrate Dr. Qi’s expertise in machine learning, sensor fusion, and biomechatronics, establishing him as a rising authority in assistive robotics. His research not only advances the theoretical foundations of exoskeleton control but also offers practical solutions for reducing hardware costs and improving user adaptability in rehabilitation and mobility assistance technologies.