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.

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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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Wright, Zachary A; Lazzaro, Emily; Thielbar, Kelly O et al. (2018) Robot Training With Vector Fields Based on Stroke Survivors' Individual Movement Statistics. IEEE Trans Neural Syst Rehabil Eng 26:307-323
Bittmann, Moria F; Patton, James L; Huang, Felix C (2017) Customized therapy using distributions of reaching errors. IEEE Int Conf Rehabil Robot 2017:658-663
Bittmann, Moria Fisher; Patton, James Lanphier (2017) Forces That Supplement Visuomotor Learning: A ""Sensory Crossover"" Experiment. IEEE Trans Neural Syst Rehabil Eng 25:1109-1116
Huang, Felix C (2017) Simulation of variable impedance as an intervention for upper extremity motor exploration. IEEE Int Conf Rehabil Robot 2017:573-578
Horowitz, Justin; Majeed, Yazan Abdel; Patton, James (2016) A fresh perspective on dissecting action into discrete submotions. Conf Proc IEEE Eng Med Biol Soc 2016:5684-5688
Huang, Felix C; Patton, James L (2016) Movement distributions of stroke survivors exhibit distinct patterns that evolve with training. J Neuroeng Rehabil 13:23
Horowitz, Justin; Patton, James (2015) I Meant to Do That: Determining the Intentions of Action in the Face of Disturbances. PLoS One 10:e0137289
Wright, Zachary A; Carlsen, Anthony N; MacKinnon, Colum D et al. (2015) Degraded expression of learned feedforward control in movements released by startle. Exp Brain Res 233:2291-300
Parmar, Pritesh N; Huang, Felix C; Patton, James L (2015) Evidence of multiple coordinate representations during generalization of motor learning. Exp Brain Res 233:1-13
Fisher, Moria E; Huang, Felix C; Wright, Zachary A et al. (2014) Distributions in the error space: goal-directed movements described in time and state-space representations. Conf Proc IEEE Eng Med Biol Soc 2014:6953-6

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