Machine Learning Analysis of Physical Activity Data to Classify Postural Dysfunction.

ABSTRACT:

Background

Machine learning (ML) analysis of biometric data in non-controlled environments is underexplored.

Objective

To evaluate whether ML analysis of physical activity data can be employed to classify whether individuals have postural dysfunction in middle-aged and older individuals.

Methods

A 1 week period of physical activity was measured by a waist-worn uni-axial accelerometer during the 2003-2004 National Health and Nutrition Examination Survey sampling period. Features of physical activity along with basic demographic information (42 variables) were paired with ML models to predict the success or failure of a standard 30 s modified Romberg test during which participants had their eyes closed and stood upon a 3-inch compliant surface. Model performance was eva

SUBMITTER: Vanstrum EB 

PROVIDER: S-EPMC10589386 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature