Nicolas Schweighofer
Papers
10
Total Citations
607
H-Index
9
About
Nicolas Schweighofer is a leading figure in computational neurorehabilitation, a field he has helped define by merging computational motor control with rehabilitation science. His research focuses on understanding the neural mechanisms of motor learning and recovery after stroke, and on developing robotic and adaptive training systems to enhance rehabilitation outcomes. A major contribution is his pioneering work on computational models that dissociate true sensorimotor recovery from compensatory strategies—a critical distinction for designing effective therapies. His 2016 paper, "Computational neurorehabilitation: modeling plasticity and learning to predict recovery," has garnered 190 citations, reflecting its foundational impact. Schweighofer also led the development of the ADAPT (Adaptive and Automatic Presentation of Tasks) robotic system, which personalizes task difficulty to maximize patient engagement and recovery. His work, including the highly cited "Computational motor control in humans and robots" (107 citations), bridges robotics and neuroscience to create evidence-based, task-oriented rehabilitation tools. By integrating machine learning, robotics, and clinical research, Schweighofer’s contributions are shaping the future of personalized stroke rehabilitation and have established him as a key innovator in the field.
Research Focus
Key Achievements
Top Papers
- 1
- 2Computational motor control in humans and robots107 citations · 2005
- 3Task-Oriented Rehabilitation Robotics72 citations · 2012
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- 5Innovative technologies applied to sensorimotor rehabilitation after stroke54 citations · 2014
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- 8Local Online Support Vector Regression for Learning Control24 citations · 2007
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