Fang Peng

South China University of Technology, University of Electronic Science and Technology of China

Papers

Total Citations

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About

Fang Peng is a robotics researcher whose work focuses on the intersection of human-robot interaction, assistive technologies, and intelligent control systems. Their primary research areas include gait analysis for rehabilitation robotics, visual tracking for robotic manipulation, and balance control for bipedal robots. Peng's most impactful contribution is the development of an IoT-assisted kernel linear discriminant analysis algorithm for gait phase detection during cognitive tasks, which addresses a critical limitation in lower-limb assistive robots by using surface electromyography signals to enable more natural walking patterns. This work, published in 2019, has accumulated 23 citations and represents a significant advance in human-robot cooperation for rehabilitation applications. In 2022, Peng further demonstrated their expertise in robotic vision with a kernel correlation filter-based method for tracking and grasping moving objects, which has garnered 19 citations for its potential in industrial and collaborative settings. While their earlier work on reinforcement learning for bipedal robot balance control received fewer citations, it laid the foundation for their current trajectory in developing intelligent, adaptive robotic systems that can respond to real-world disturbances.