Exploring unsupervised feature extraction of IMU-based gait data in stroke rehabilitation using a variational autoencoder.

ABSTRACT:

Background

Variational AutoEncoders (VAE) might be utilized to extract relevant information from an IMU-based gait measurement by reducing the sensor data to a low-dimensional representation. The present study explored whether VAEs can reduce IMU-based gait data of people after stroke into a few latent features with minimal reconstruction error. Additionally, we evaluated the psychometric properties of the latent features in comparison to gait speed, by assessing 1) their reliability; 2) the difference in scores between people after stroke and healthy controls; and 3) their responsiveness during rehabilitation.

Methods

We collected test-retest and longitudinal two-minute walk-test data using an IMU from people after stroke in clinical rehabilitation, as well as from a health

SUBMITTER: Felius R 

PROVIDER: S-EPMC11452054 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature