Courtney Celian
Papers
6
Total Citations
26
H-Index
4
About
Courtney Celian is a rising force in neurorehabilitation engineering, whose work is redefining how stroke survivors recover upper limb function. Her research sits at the intersection of motor learning, robotic therapy, and visual feedback, with a bold focus on leveraging error augmentation—not error reduction—to drive neural repair. In her most cited work, Celian demonstrated that distorted visual feedback, perceived as forces, can foster motor relearning without expensive robotic hardware, a finding with 8 citations that challenges conventional rehabilitation timelines. She has since advanced this concept with personalized robotic training algorithms, using neuro-adaptive control to generate customized perturbation forces that accelerate motor adaptation. Her 2025 papers on error fields and isometric training generalization (each garnering 4 and 3 citations) explore how skills learned in one workspace or under static conditions can transfer to unpracticed environments, offering scalable, low-cost interventions. Celian also developed PRISM, a synthetic modeling framework for identifying human motor learning parameters, addressing a long-standing gap in system identification. Her work is not just incremental—it is foundational, proposing that therapy should be dynamic, personalized, and error-driven. For students and researchers, Celian represents a new wave of rehabilitation science: one that treats the brain not as a broken machine, but as a system that learns best when challenged.
Research Focus
Key Achievements
Top Papers
- 1
- 2Stroke Rehabilitation with Distorted Vision Perceived as Forces5 citations · 2019
- 3
- 4Personalized Robotic Training on a Planar Reaching Task4 citations · 2025
- 5
- 6PRISM: Parameter Recovery Identification from Synthetic Modeling2 citations · 2025