One of the most pervasive problems for stroke survivors is movement deficits. Recent research strongly supports prolonged practice of functionally-relevant activities of the upper limb, even though therapy time is quite limited by the current medical economic system. This grant focuses on new developments in human- robot interactions (haptics) that have revealed prospects in the areas of motor teaching and rehabilitation. Specialized robotic devices combined with computer-displays can tirelessly exert force, augment feedback, and redirect error in order to speed up, enhance, or trigger the motor relearning process. These approaches could extend and greatly enhance the recovery process. The first strategy that often comes to mind for teaching movements is to guide the limb along the desired path. However, a promising alternative approach is to make movements more difficult by deflecting them from the desired path. People develop, through practice, the ability to counteract forces that distort the mechanical world, and if these forces are properly designed and applied, a desired movement pattern occurs when the forces are eventually switched off. We and others have also obtained similar results by distorting the visual world using prisms or virtual reality displays. In these studies, the subject sees something unexpected that is perceived as an error. Our results point to a single unifying theory: Errors induce learning, and judicious error augmentation (through forces or visual distortions) can lead to lasting desired changes. Interestingly, this process appears to bypass conventional learning mechanisms that require intense concentration - results are the same if the subjects have a conversation or listen to music. They often consider it a game. Until now very little of this research has been functionally relevant because the devices'ranges of motion were small, were two dimensional, and were lacking an appropriate visual interface. Three dimensional movements introduce the daunting new challenge of gravitational effects that could reduce (or perhaps heighten) the potential of error augmentation training. Our lab has spent several years developing a large- workspace, three dimensional haptics/graphics system.
The aims of this grant are to build on our promising body of evidence and expand our error augmentation training work to a large workspace in three dimensions. Accordingly, the experiments below further refine our understanding of error augmentation (Aim 1), expand our approaches to three dimensions (Aim 2), and then move towards clinical application by testing our approaches on stroke survivors (Aim 3).

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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