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About

Guowei Huang is a leading researcher in rehabilitation robotics, with a focus on developing intelligent, adaptive systems for human motor recovery. His work centers on lower limb exoskeletons and upper limb physiotherapy robots, where he has made significant contributions to personalized gait generation and assistive control. Huang’s most cited paper, "Individualized Gait Generation for Rehabilitation Robots Based on Recurrent Neural Networks" (2020, 36 citations), pioneers a method to create customized reference gait patterns at continuously varying speeds—overcoming the limitations of fixed-speed approaches and greatly enhancing rehabilitation effectiveness. He also investigated the mechanical efficiency of ankle-assisted robots during load-carriage walking (2021, 6 citations), demonstrating real-world applications for wearable assistive devices. In upper limb therapy, Huang designed the Dual-Arm End-Effector-Based Rehabilitation Robot (DEREROB) for stroke patients (2018, 5 citations), showcasing his versatility across both upper and lower extremity robotics. His most recent work (2024) compares Transformer and LSTM deep learning models for predicting exoskeleton gait trajectories from plantar pressure data, pushing the boundaries of real-time, sensor-driven control. With a growing citation impact, Huang’s research is shaping the next generation of personalized, data-driven rehabilitation technologies.