Papers
8
Total Citations
93
H-Index
4
About
Qichuan Ding is a leading researcher in robotics and human-machine interaction, with a focus on fault diagnosis, myoelectric control, and wearable exoskeleton systems. His work bridges critical gaps in industrial robotics and assistive technologies, particularly through innovative signal processing and machine learning approaches. Ding’s most cited paper, “Fault Diagnosis of Harmonic Drives Based on an SDP-ConvNeXt Joint Methodology” (2023, 29 citations), introduces a cutting-edge deep learning framework to detect failures in harmonic drives—essential components of industrial robots—preventing costly operational accidents. His earlier contributions include a novel EMG-driven state-space model for estimating human joint motion (2011, 27 citations), which has advanced the control of prostheses and exoskeletons. Ding also developed an incremental learning and fault-tolerant classifier (2022, 13 citations) to maintain myoelectric pattern recognition stability against sudden signal interferences, enhancing the safety of assistive robots. His work on skeleton-based action recognition via graph convolutional networks (2022, 10 citations) and exoskeleton arm configuration optimization (2017, 4 citations) further demonstrates his versatility. With over 90 total citations, Ding’s research is pivotal for reliable, adaptive robotic systems in both industrial and rehabilitation settings.
Research Focus
Key Achievements
Top Papers
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- 2A Novel Motion Estimate Method of Human Joint with EMG-Driven Model27 citations · 2011
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