Papers
12
Total Citations
235
H-Index
8
About
Xingda Qu is a prominent researcher specializing in robotic gait rehabilitation, biomechanics, and assistive technologies for mobility impairment and aging populations. His work has made significant contributions to the development of intelligent, patient-specific rehabilitation systems that bridge the gap between clinical need and engineering innovation. Qu's most influential contribution is his development of individualized gait pattern prediction models using generalized regression neural networks, which has garnered 76 citations and transformed how robotic rehabilitation systems adapt to individual patients. His work on the NaTUre-Gaits over-ground gait trainer (58 citations) represents a landmark achievement in hardware development for body weight supported rehabilitation, directly addressing therapist shortages and the labor-intensive nature of traditional gait therapy for stroke and spinal cord injury patients. Throughout the 2010s, Qu consistently advanced subject-specific gait planning methodologies, demonstrating how artificial neural networks could generate natural, smooth movement patterns tailored to individual physiological parameters. More recently, his research has expanded to fall prevention in older adults through wearable soft robotics (2021), reflecting a broadening impact across aging and rehabilitation medicine. With over 230 cumulative citations, Qu's body of work continues to shape the future of intelligent, personalized rehabilitation engineering.
Research Focus
Key Achievements
Top Papers
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- 3Subject-specific lower limb waveforms planning via artificial neural network20 citations · 2011
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- 5A Soft Robotic Intervention for Gait Enhancement in Older Adults14 citations · 2021
- 6Subject tailored gait pattern planning for robotic gait rehabilitation12 citations · 2010
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