Exploration and demonstration of explainable machine learning models in prosthetic rehabilitation-based gait analysis.

ABSTRACT: Quantitative gait analysis is important for understanding the non-typical walking patterns associated with mobility impairments. Conventional linear statistical methods and machine learning (ML) models are commonly used to assess gait performance and related changes in the gait parameters. Nonetheless, explainable machine learning provides an alternative technique for distinguishing the significant and influential gait changes stemming from a given intervention. The goal of this work was to demonstrate the use of explainable ML models in gait analysis for prosthetic rehabilitation in both population- and sample-based interpretability analyses. Models were developed to classify amputee gait with two types of prosthetic knee joints. Sagittal plane gait patterns of 21 individuals with unilate

SUBMITTER: Aghababa MP 

PROVIDER: S-EPMC10987001 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature