Yanjian Liao
Papers
1
Total Citations
7
H-Index
1
About
Yanjian Liao is a researcher in biomedical engineering, with a focus on human–machine interaction and surface electromyography (sEMG)-based gesture recognition. His work addresses a critical challenge in prosthetics and wearable technology: improving the accuracy of hand gesture classification when muscle activities are similar. In his most cited study (2018, 7 citations), Liao systematically investigated how non-uniform sample assignment in training sets affects recognition efficiency—a largely unexplored factor at the time. By demonstrating that strategic arrangement of sEMG feature samples can significantly enhance classification performance, he provided a practical, data-driven approach to refining machine learning models for prosthetic control. This contribution is especially valuable for developing more intuitive and responsive assistive devices. Though early in his career, Liao’s work has already informed subsequent studies in sEMG pattern recognition, and his methodological insights continue to guide researchers seeking to improve the robustness of gesture recognition systems in real-world applications.
Research Focus
Key Achievements
Top Papers
- 1