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Discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics

Locomotion results from the interactions of highly nonlinear neural and biomechanical dynamics. Accordingly, understanding gait dynamics across behavioral conditions and individuals based on detailed modeling of the underlying neuromechanical system has proven difficult. Here, we develop a data-driv...

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Autores principales: Winner, Taniel S., Rosenberg, Michael C., Jain, Kanishk, Kesar, Trisha M., Ting, Lena H., Berman, Gordon J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10610102/
https://www.ncbi.nlm.nih.gov/pubmed/37889927
http://dx.doi.org/10.1371/journal.pcbi.1011556
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author Winner, Taniel S.
Rosenberg, Michael C.
Jain, Kanishk
Kesar, Trisha M.
Ting, Lena H.
Berman, Gordon J.
author_facet Winner, Taniel S.
Rosenberg, Michael C.
Jain, Kanishk
Kesar, Trisha M.
Ting, Lena H.
Berman, Gordon J.
author_sort Winner, Taniel S.
collection PubMed
description Locomotion results from the interactions of highly nonlinear neural and biomechanical dynamics. Accordingly, understanding gait dynamics across behavioral conditions and individuals based on detailed modeling of the underlying neuromechanical system has proven difficult. Here, we develop a data-driven and generative modeling approach that recapitulates the dynamical features of gait behaviors to enable more holistic and interpretable characterizations and comparisons of gait dynamics. Specifically, gait dynamics of multiple individuals are predicted by a dynamical model that defines a common, low-dimensional, latent space to compare group and individual differences. We find that highly individualized dynamics–i.e., gait signatures–for healthy older adults and stroke survivors during treadmill walking are conserved across gait speed. Gait signatures further reveal individual differences in gait dynamics, even in individuals with similar functional deficits. Moreover, components of gait signatures can be biomechanically interpreted and manipulated to reveal their relationships to observed spatiotemporal joint coordination patterns. Lastly, the gait dynamics model can predict the time evolution of joint coordination based on an initial static posture. Our gait signatures framework thus provides a generalizable, holistic method for characterizing and predicting cyclic, dynamical motor behavior that may generalize across species, pathologies, and gait perturbations.
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spelling pubmed-106101022023-10-28 Discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics Winner, Taniel S. Rosenberg, Michael C. Jain, Kanishk Kesar, Trisha M. Ting, Lena H. Berman, Gordon J. PLoS Comput Biol Research Article Locomotion results from the interactions of highly nonlinear neural and biomechanical dynamics. Accordingly, understanding gait dynamics across behavioral conditions and individuals based on detailed modeling of the underlying neuromechanical system has proven difficult. Here, we develop a data-driven and generative modeling approach that recapitulates the dynamical features of gait behaviors to enable more holistic and interpretable characterizations and comparisons of gait dynamics. Specifically, gait dynamics of multiple individuals are predicted by a dynamical model that defines a common, low-dimensional, latent space to compare group and individual differences. We find that highly individualized dynamics–i.e., gait signatures–for healthy older adults and stroke survivors during treadmill walking are conserved across gait speed. Gait signatures further reveal individual differences in gait dynamics, even in individuals with similar functional deficits. Moreover, components of gait signatures can be biomechanically interpreted and manipulated to reveal their relationships to observed spatiotemporal joint coordination patterns. Lastly, the gait dynamics model can predict the time evolution of joint coordination based on an initial static posture. Our gait signatures framework thus provides a generalizable, holistic method for characterizing and predicting cyclic, dynamical motor behavior that may generalize across species, pathologies, and gait perturbations. Public Library of Science 2023-10-27 /pmc/articles/PMC10610102/ /pubmed/37889927 http://dx.doi.org/10.1371/journal.pcbi.1011556 Text en © 2023 Winner et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Winner, Taniel S.
Rosenberg, Michael C.
Jain, Kanishk
Kesar, Trisha M.
Ting, Lena H.
Berman, Gordon J.
Discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics
title Discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics
title_full Discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics
title_fullStr Discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics
title_full_unstemmed Discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics
title_short Discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics
title_sort discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10610102/
https://www.ncbi.nlm.nih.gov/pubmed/37889927
http://dx.doi.org/10.1371/journal.pcbi.1011556
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