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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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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. |
format | Online Article Text |
id | pubmed-10610102 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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