Skip to main content

ETHNOS_APP

Home • Search • Journals • List 0

Fine synergies" describe motor adaptation in people with drop foot in a way that supplements traditional "coarse synergies

Bibliographic Data

ID5285920
AuthorsAngelo Bartsch-Jimenez, Angelo Bartsch‐jiménez (University of Valparaíso), Michalina Błażkiewicz (0000-0001-8452-6824, Józef Piłsudski University of Physical Education in Warsaw), Hesam Azadjou (University of Southern California), Ryan Novotny (0000-0002-3484-4514, University of Southern California), Francisco J Valero-Cuevas, Francisco J Valero‐cueva (0000-0002-2611-7923, University of Southern California, corresponding author)
Year2023
Volume5
Pages1080170-1080170
Publication date2023-02-17
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Sports and Active Living (JOURNAL)
Journal identifiersISSN: 2624-9367 • E-ISSN: 2624-9367
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fspor.2023.1080170
PMID36873662
OpenAlexW4321241608
LanguageEN
Citations received1
References cited25

Synergy analysis via dimensionality reduction is a standard approach in biomechanics to capture the dominant features of limb kinematics or muscle activation signals, which can be called "coarse synergies." Here we demonstrate that the less dominant features of these signals, which are often explicitly disregarded or considered noise, can nevertheless exhibit "fine synergies" that reveal subtle, yet functionally important, adaptations. To find the coarse synergies, we applied non-negative matrix factorization (NMF) to unilateral EMG data from eight muscles of the involved leg in ten people with drop-foot (DF), and of the right leg of 16 unimpaired (control) participants. We then extracted the fine synergies for each group by removing the coarse synergies (i.e., first two factors explaining ≥85% of variance) from the data and applying Principal Component Analysis (PCA) to those residuals. Surprisingly, the time histories and structure of the coarse EMG synergies showed few differences between DF and controls-even though the kinematics of drop-foot gait is evidently different from unimpaired gait. In contrast, the structure of the fine EMG synergies (as per their PCA loadings) showed significant differences between groups. In particular, loadings for Tibialis Anterior, Peroneus Longus, Gastrocnemius Lateralis, Biceps and Rectus Femoris, Vastus Medialis and Lateralis muscles differed between groups (p<0.05). We conclude that the multiple differences found in the structure of the fine synergies extracted from EMG in people with drop-foot vs. unimpaired controls-not visible in the coarse synergies-likely reflect differences in their motor strategies. Coarse synergies, in contrast, seem to mostly reflect the gross features of EMG in bipedal gait that must be met by all participants-and thus show few differences between groups. However, drawing insights into the clinical origin of these differences requires well-controlled clinical trials. We propose that fine synergies should not be disregarded in biomechanical analysis, as they may be more informative of the disruption and adaptation of muscle coordination strategies in participants due to drop-foot, age and/or other gait impairments

Ankle · Biceps · Biology · Biomechanics · Contrast (vision · Electromyography · Gait · Kinematics · Leg muscle · Motor Control · Physical medicine and rehabilitation · Physics · Principal component analysis · Vastus medialis · Cerebral Palsy and Movement Disorders · Computer Science · Mathematics · Medicine · Motor Control and Adaptation · Muscle activation and electromyography studies · Neuroscience · Anatomy · Artificial Intelligence

  • Neuromuscular control

    Open Access•Daanish M Mulla, Peter J Keir•Frontiers in Sports and Active…•2023

  • Robust statistical methods in R using the WRS2 package

    Open Access•Patrick Mair, Rand Wilcox et al.•Behavior Research Methods•2020

  • Learning the parts of objects by non-negative matrix factorization

    Open Access•Daniel D Lee, H Sebastian Seung•Nature•1999

Unique citing works1
Citations per year0,33
Citation span2023 - 2023 (1)
Citation velocityhistorical
Highly citedNo
Citation typesNeutral: 1

Tools

Open DOIOpen Access
Ethnos_APP • Open Source Project • MIT License • Frontend v2.0.0 • Privacy and Cookies • API Documentation: api.ethnos.app/docs • API Source Code: GitHub • DOI: 10.5281/zenodo.17049435 • Frontend Source Code: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae