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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS053606-06
Application #
8660714
Study Section
Musculoskeletal Rehabilitation Sciences Study Section (MRS)
Program Officer
Chen, Daofen
Project Start
2005-12-01
Project End
2018-06-30
Budget Start
2014-07-01
Budget End
2015-06-30
Support Year
6
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Rehabilitation Institute of Chicago
Department
Type
DUNS #
City
Chicago
State
IL
Country
United States
Zip Code
60611
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
Abdollahi, Farnaz; Case Lazarro, Emily D; Listenberger, Molly et al. (2014) Error augmentation enhancing arm recovery in individuals with chronic stroke: a randomized crossover design. Neurorehabil Neural Repair 28:120-8
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
Wright, Zachary A; Fisher, Moria E; Huang, Felix C et al. (2014) Data sample size needed for prediction of movement distributions. Conf Proc IEEE Eng Med Biol Soc 2014:5780-3
Huang, Felix C; Patton, James L (2013) Augmented dynamics and motor exploration as training for stroke. IEEE Trans Biomed Eng 60:838-44
Abdollahi, Farnaz; Kenyon, Robert V; Patton, James L (2013) Mirror versus parallel bimanual reaching. J Neuroeng Rehabil 10:71
Huang, Felix C; Patton, James L (2013) Individual patterns of motor deficits evident in movement distribution analysis. IEEE Int Conf Rehabil Robot 2013:6650430

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