Papers
35
Total Citations
989
H-Index
19
About
Dingguo Zhang is a pioneering researcher at the intersection of rehabilitation robotics, human-machine interfaces, and assistive technology. His work centers on developing intelligent systems that restore and augment motor function in individuals with neurological and musculoskeletal impairments, spanning robotic exoskeletons, brain-computer interfaces (BCIs), and functional electrical stimulation (FES). Zhang's most influential contributions include adaptive EMG-based torque estimation methods for lower-limb exoskeletons (166 citations) and multimodal human-robot interaction frameworks that actively engage rehabilitation patients rather than passively guiding them (112 citations). His research on non-invasive BCI-controlled robotic arms—employing shared control strategies that blend user intent with computer vision—has significantly advanced the practical usability of brain-actuated assistive devices, accumulating over 200 citations across multiple studies. His hybrid FES-exoskeleton systems for both upper and lower limbs represent innovative translational engineering, bridging neurostimulation and wearable robotics. Beyond hardware, Zhang has contributed to flexible robotics and dynamic modeling, including soft robotic fish driven by dielectric elastomers. His 2021 perspective on wearable assistive robotics has helped shape the field's future research agenda. Collectively, his portfolio reflects a sustained commitment to improving independence and quality of life for people with motor disabilities through rigorous, human-centered engineering.
Research Focus
Key Achievements
Top Papers
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- 8Impact dynamics of flexible-joint robots37 citations · 2004
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