Longjie Yu
Papers
2
Total Citations
25
H-Index
2
About
Longjie Yu is a rising force in the field of rehabilitation robotics and human motion analysis, with a sharp focus on wearable sensor technology and machine learning. His research centers on decoding complex human movements—particularly the sit-to-stand transition and complete gait cycles—to enable smarter, more responsive control of exoskeletons and assistive devices. Yu’s most cited work introduces a novel CNN-BiLSTM ensemble model enhanced with an attention mechanism, achieving robust phase identification using just two inertial sensors; this paper has already garnered 22 citations since its 2024 publication, underscoring its immediate impact. He further pushes boundaries by integrating muscle synergy analysis with a PSO-optimized CNN-LSTM algorithm to recognize seven distinct phases of the stand-walk-stand cycle, a breakthrough for continuous daily walking rehabilitation. By fusing biomechanics with deep learning, Yu is not only advancing real-time exoskeleton control but also laying the groundwork for personalized, data-driven therapy. His work promises to transform how patients with mobility impairments regain independence, making him a researcher to watch in the intersection of AI and human movement science.
Research Focus
Key Achievements
Top Papers
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