Identification of distinct subtypes of post-stroke and neurotypical gait behaviors using neural network analysis of kinematic time series data.

ABSTRACT:

Background

Heterogeneous types of gait impairment are common post-stroke. Studies used supervised and unsupervised machine learning on discrete biomechanical features to summarize the gait cycle and identify common patterns of gait behaviors. However, discrete features cannot account for temporal variations that occur during gait. Here, we propose a novel machine-learning pipeline to identify subgroups of gait behaviors post-stroke using kinematic time series data.

Methods

We analyzed ankle and knee kinematic data during treadmill walking data in 39 individuals post-stroke and 28 neurotypical controls. The data were first input into a supervised dual-stage Convolutional Neural Network-Temporal Convolutional Network, trained to extract temporal and spatial gait features. Then

SUBMITTER: Kuch A 

PROVIDER: S-EPMC11565882 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature