Qiliang Xiong
Papers
1
Total Citations
7
H-Index
1
About
Qiliang Xiong is a researcher focused on advancing human-machine interaction through surface electromyography (sEMG)-based gesture recognition. His work addresses a critical challenge in the field: improving classification accuracy for hand gestures that involve similar muscle activities, which often leads to confusion in conventional machine learning models. Xiong’s most cited paper, “Non-Uniform Sample Assignment in Training Set Improving Recognition of Hand Gestures Dominated with Similar Muscle Activities” (2018, 7 citations), introduces a novel approach to training set design. By systematically exploring how sample assignment of sEMG features affects recognition efficiency, he demonstrates that non-uniform sample arrangements can significantly enhance classification performance. This work provides foundational insights into optimizing training data for gesture recognition systems, with implications for prosthetics, rehabilitation, and wearable technology. Though early in his career, Xiong’s contributions are already shaping how researchers approach data preparation in biosignal processing. His findings offer a practical pathway to more robust and intuitive control interfaces, making his research highly relevant for students and engineers working on next-generation human-computer interaction systems.
Research Focus
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Top Papers
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