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

ABSTRACT: 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 treadm

SUBMITTER: Winner TS 

PROVIDER: S-EPMC10610102 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature