Fengxue Li

Papers

Total Citations

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About

Fengxue Li is a researcher whose work bridges the frontiers of haptic technology and machine learning, with a particular focus on advancing human-computer interaction through tactile sensing. Li’s most cited paper, "Haptic recognition using hierarchical extreme learning machine with local-receptive-field" (2017, 8 citations), introduces a novel approach that combines hierarchical extreme learning machines with local-receptive-field structures to improve the accuracy and efficiency of haptic pattern recognition. This contribution is significant for enabling more intuitive and responsive robotic systems, prosthetics, and virtual reality interfaces. By leveraging the speed and simplicity of extreme learning machines while incorporating biologically inspired local receptive fields, Li’s work offers a computationally efficient method for processing tactile data, which is critical for real-time applications. Though still early in their career, Li’s research has already attracted attention from peers working in robotics and sensory systems, laying a foundation for future innovations in how machines perceive and respond to touch. Their work exemplifies the growing importance of integrating advanced learning algorithms with physical sensing to create more adaptive and human-like technologies.