This study investigates the potential of customized robotic and visual feedback interaction to improve recovery of movements in stroke survivors. While therapists widely recognize that customization is critical to recovery, little is understood about how take advantage of statistical analysis tools to aid in the process of designing individualized training. Our approach first creates a model of a person's own unique movement deficits, and then creates a practice environment to correct these problems. Experiments will determine how the deficit-field approach can improve (1) reaching accuracy, (2) range of motion, and (3) activities of daily living. The findings will not only shed light on how to improve therapy for stroke survivors, it will test hypotheses about fundamental processes of practice and learning. This study will help us move closer to our long-term goal of clinically effective treatments using interactive devices.

Public Health Relevance

This study investigates how robotic interaction and visual feedback can improve recovery of movements in stroke survivors, customized using statistical models of individual movement errors. We determine how our approach can be used to restore reaching accuracy, range of motion, and functional ability in activities of daily living.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Research Project (R01)
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Musculoskeletal Rehabilitation Sciences Study Section (MRS)
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Chen, Daofen
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Rehabilitation Institute of Chicago
United States
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