Liangjie Tu
Papers
2
Total Citations
12
H-Index
1
About
Liangjie Tu is a rising researcher in the field of rehabilitation robotics and human motion analysis, with a primary focus on the continuous prediction of lower limb joint angles and gait trajectories using surface electromyography (sEMG) signals. His work addresses critical challenges in exoskeleton control and stroke rehabilitation, aiming to enable seamless, coordinated assistance between healthy and paretic limbs. Tu’s major contributions include the development of advanced hybrid machine learning models—such as the ISSA-HKELM and ISSA-CNN-SVR algorithms—which integrate optimization techniques with kernel and deep learning methods to achieve accurate, real-time motion prediction. His most-cited paper (2024, 11 citations) introduces a wearable sEMG system combined with an improved sparrow search algorithm for continuous hip and knee angle estimation, while his 2025 work extends this approach to predict paretic gait trajectories for stroke patients. Though early in his career, Tu’s work has already garnered attention for its practical implications in assistive robotics and neurorehabilitation, demonstrating strong potential for impactful contributions to human-robot interaction and motor recovery technologies.
Research Focus
Key Achievements
Top Papers
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