Dingxun Jin
Papers
2
Total Citations
13
H-Index
1
About
Dingxun Jin is a rising researcher in biomedical engineering and human–robot interaction, whose work focuses on decoding complex lower limb movements from surface electromyography (sEMG) signals. His primary research areas include muscle synergy analysis, deep learning for biosignal processing, and continuous motion recognition for intelligent assistive robotics. Jin’s most cited work, “Integration of multiscale fusion of residual neural network with 2-D gramian angular fields for lower limb movement recognition based on multi-channel sEMG signals” (2024, 12 citations), introduces a novel approach that transforms temporal sEMG data into image-like representations, enabling a multiscale residual network to achieve high-accuracy movement classification. More recently, his 2025 paper on “Muscle Synergy-driven TimesNet Method for Continuous Recognition of Lower Limb Motion Patterns” advances the field by addressing a critical limitation: most existing methods require complete gait cycles as input, hindering real-time control. Jin’s synergy-driven TimesNet framework enables continuous, cycle-independent recognition, a significant step toward seamless human–robot collaboration. Though early in his career, Jin’s innovative fusion of physiological priors with modern deep learning architectures marks him as a promising contributor to the development of more responsive and intuitive lower-limb assistive devices.
Research Focus
Key Achievements
Top Papers
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