NDLI: Learning, Not Adaptation, Characterizes Stroke Motor Recovery: Evidence From Kinematic Changes Induced by Robot-Assisted Therapy in Trained and Untrained Task in the Same Workspace

Content Provider	IEEE Xplore Digital Library
Author	Dipietro, L. Krebs, H.I. Volpe, B.T. Stein, J. Bever, C. Mernoff, S.T. Fasoli, S.E. Hogan, N.
Copyright Year	2001
Abstract	Both the American Heart Association and the VA/DoD endorse upper-extremity robot-mediated rehabilitation therapy for stroke care. However, we do not know yet how to optimize therapy for a particular patient's needs. Here, we explore whether we must train patients for each functional task that they must perform during their activities of daily living or alternatively capacitate patients to perform a class of tasks and have therapists assist them later in translating the observed gains into activities of daily living. The former implies that motor adaptation is a better model for motor recovery. The latter implies that motor learning (which allows for generalization) is a better model for motor recovery. We quantified trained and untrained movements performed by 158 recovering stroke patients via 13 metrics, including movement smoothness and submovements. Improvements were observed both in trained and untrained movements suggesting that generalization occurred. Our findings suggest that, as motor recovery progresses, an internal representation of the task is rebuilt by the brain in a process that better resembles motor learning than motor adaptation. Our findings highlight possible improvements for therapeutic algorithms design, suggesting sparse-activity-set training should suffice over exhaustive sets of task specific training.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	10
File Size	1057312
Starting Page	48
Ending Page	57
File Format	PDF
ISSN	15344320
Volume Number	20
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robots Measurement Medical treatment Kinematics Discharges Adaptation models Training stroke motor adaptation motor learning rehabilitation robotics
Content Type	Text
Resource Type	Article
Subject	Neuroscience Rehabilitation Internal Medicine Biomedical Engineering Computer Science Applications

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