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Submovements Grow Larger, Fewer, and More Blended during Stroke Recovery

Rohrer, Brandon, Fasoli, Susan, Krebs, Hermano Igo, Volpe, Bruce, Frontera, Walter R, Stein, Joel, Hogan, Neville·2004·Motor Control

Abstract

Submovements are hypothesized building blocks of human movement, discrete ballistic movements of which more complex movements are composed. Using a novel algorithm, submovements were extracted from the point-to-point movements of 41 persons recovering from stroke. Analysis of the extracted submovements showed that, over the course of therapy, patients' submovements tended to increase in peak speed and duration. The number of submovements employed to produce a given movement decreased. The time between the peaks of adjacent submovements decreased for inpatients (those less than 1 month post-stroke), but not for outpatients (those greater than 12 months post-stroke) as a group. Submovements became more overlapped for all patients, but more markedly for inpatients. The strength and consistency with which it quantified patients' recovery indicates that analysis of submovement overlap might be a useful tool for measuring learning or other changes in motor behavior in future human movement studies.

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Metadata

Authors
Rohrer, B., Fasoli, S., Krebs, H. I., Volpe, B., Frontera, W. R., Stein, J., & Hogan, N.
Venue
Motor Control
Published
2004-10
Volume
8
Issue
4
Pages
472-483
ISSN
  • 1087-1640 (ISSN-L)
  • 1543-2696
OpenAlex
W2131292708
Cited by
163
References
0
FWCI
1.76
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Submovements Grow Larger, Fewer, and More Blended during Stroke Recovery. https://app.cochat.ai/papers/10.1123/mcj.8.4.472
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Rohrer, B., Fasoli, S., Krebs, H. I., Volpe, B., Frontera, W. R., Stein, J., & Hogan, N. (2004). Submovements Grow Larger, Fewer, and More Blended during Stroke Recovery. Motor Control. https://doi.org/10.1123/mcj.8.4.472

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.