Linjie Liu

Papers

Total Citations

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About

Linjie Liu is a researcher whose work lies at the intersection of biomedical signal processing and machine learning, with a particular focus on surface electromyography (sEMG) and its applications in human–machine interaction. Her most cited study, "Research on Gesture Recognition of Surface EMG Based on Machine Learning" (2022), explores how machine learning algorithms can decode muscle activity patterns to accurately classify hand gestures. This work contributes to the development of intuitive, non-invasive control systems for prosthetics, rehabilitation devices, and wearable technology. By leveraging sEMG signals, Liu’s research addresses key challenges in real-time gesture recognition, such as signal variability and classification accuracy. While her citation count is still building—reflecting the early stage of her career—her work has already drawn attention for its practical implications in assistive technology and robotics. Liu’s contributions are particularly relevant for students and researchers interested in the convergence of artificial intelligence and biomedical engineering, offering a foundation for future innovations in intuitive human–computer interfaces.