Xiufen Xin

Papers

Total Citations

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About

Xiufen Xin is a rising researcher in the field of rehabilitation robotics, with a focused interest in applying deep learning and intelligent control to improve human-robot interaction for patients with motor dysfunction. Her work primarily addresses the critical challenge of generating personalized, adaptive gait patterns for lower limb exoskeletons used in the rehabilitation of individuals with neurological disorders such as stroke and spinal cord injury. Her most cited paper, "Gait Prediction for Rehabilitation Robots Based on Deep Learning" (2022, 7 citations), proposes a predictive model that anticipates a wearer’s intended gait trajectory, significantly enhancing the safety and naturalness of human-robot cooperation. Building on this, her 2023 work introduces the GLS network for individual gait generation, tackling the pressing need for personalized therapy in the face of limited rehabilitation resources. Though early in her career, Xin’s contributions are notable for their direct clinical relevance, aiming to transition rehabilitation robots from rigid, pre-programmed assistance to intelligent, patient-responsive partners. Her research stands at the intersection of biomechanics, control systems, and artificial intelligence, promising to make robotic therapy more accessible and effective.