Papers
2
Total Citations
13
H-Index
2
About
Liyu Wei is a rising researcher in rehabilitation robotics and human–machine interaction, with a focus on intelligent lower-limb motion analysis. Their work bridges deep learning and biomedical signal processing to advance exoskeleton control and assistive technologies. Wei’s most cited paper, “A lightweight multi-scale convolutional attention network for lower limb motion recognition with transfer learning” (2024, 8 citations), introduces an efficient architecture that reduces computational burden while maintaining high accuracy—critical for real-time wearable applications. Their 2025 study, “sEMG-Based Knee Angle Prediction: An Efficient Framework With XGBoost Feature Selection and Multiattention LSTM” (5 citations), tackles a core challenge in rehabilitation robotics: predicting joint angles from surface electromyography (sEMG) signals. By integrating XGBoost-driven feature selection with a multi-attention LSTM model, Wei’s framework achieves precise, low-latency knee angle estimation, enabling more natural human–exoskeleton interaction. Although early in their career, Wei’s work demonstrates a clear trajectory toward practical, computationally efficient solutions for assistive robotics. Their contributions are particularly relevant for researchers developing adaptive, real-time control systems for rehabilitation and mobility assistance.
Research Focus
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Top Papers
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