Papers
3
Total Citations
16
H-Index
3
About
Weixiao Li is a rising researcher in the field of intelligent robotics and human–machine interaction, with a primary focus on lower limb motion recognition and assistive exoskeleton control. Li’s work centers on integrating surface electromyography (sEMG) with deep learning to decode human movement intentions, a critical step toward more responsive prosthetics and wearable robots. In their most cited work, Li proposed a lightweight multi-scale convolutional attention network that leverages transfer learning for lower limb motion recognition, achieving efficient and accurate classification while reducing computational overhead (8 citations). Earlier contributions include an efficient sEMG-based method for identifying lower limb behavior intentions, emphasizing high recognition speed for real-time exoskeleton control (4 citations). Li has also explored the kinematics of walking robots, analyzing a novel dual Stewart parallel mechanism for mobile legs, demonstrating strong load capacity and stability (4 citations). By combining biomechanical analysis with advanced neural architectures, Li is helping to bridge the gap between biological signals and robotic actuation, paving the way for more intuitive and adaptive assistive technologies.
Research Focus
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