Papers
4
Total Citations
17
H-Index
2
About
Dingan Song is a researcher at the forefront of human-robot interaction and remote handling technologies, with a focus on rehabilitation robotics and nuclear fusion maintenance systems. His work bridges virtual reality, exoskeleton design, and digital twin systems to enhance both therapeutic outcomes and operational efficiency in high-stakes environments. Song’s most cited study, “Enhancing experience: Investigating the impact of different personal perspectives in virtual reality with lower limb rehabilitation robots” (2023, 10 citations), explores how VR perspectives influence patient motivation and engagement during robotic rehabilitation—a contribution that has garnered attention for its potential to improve recovery experiences. In the realm of fusion energy, Song has advanced tokamak maintenance through innovations like the optimized FABRIK kinematic solver and spatial partition strategies for remote O&M robots (2024, 3 citations), as well as the electro-hydraulic mix-drive cantilever arm platform (EMCAP) for general remote handling research (2024, 2 citations). His work on rope-driven flexible exoskeletons (2023, 2 citations) further demonstrates his commitment to assistive robotics, focusing on comfort and oxygen metabolism testing. With a growing citation record and applications spanning rehabilitation and nuclear engineering, Song is shaping the future of adaptive robotic systems that prioritize human factors and real-world reliability.
Research Focus
Key Achievements
Top Papers
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