Factors influencing neuromuscular responses to gait training with a robotic ankle exoskeleton in cerebral palsy.

ABSTRACT: A current limitation in the development of robotic gait training interventions is understanding the factors that predict responses to treatment. The purpose of this study was to explore the application of an interpretable machine learning method, Bayesian Additive Regression Trees (BART), to identify factors influencing neuromuscular responses to a resistive ankle exoskeleton in individuals with cerebral palsy (CP). Eight individuals with CP (GMFCS levels I - III, ages 12-18 years) walked with a resistive ankle exoskeleton over seven visits while we measured soleus activation. A BART model was developed using a predictor set of kinematic, device, study, and participant metrics that were hypothesized to influence soleus activation. The model (R² = 0.94) found that k

SUBMITTER: Conner BC 

PROVIDER: S-EPMC10070554 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature