Wearable airbag technology and machine learned models to mitigate falls after stroke.

ABSTRACT:

Background

Falls are a common complication experienced after a stroke and can cause serious detriments to physical health and social mobility, necessitating a dire need for intervention. Among recent advancements, wearable airbag technology has been designed to detect and mitigate fall impact. However, these devices have not been designed nor validated for the stroke population and thus, may inadequately detect falls in individuals with stroke-related motor impairments. To address this gap, we investigated whether population-specific training data and modeling parameters are required to pre-detect falls in a chronic stroke population.

Methods

We collected data from a wearable airbag's inertial measurement units (IMUs) from individuals with (n = 20 stroke) and without (n = 15

SUBMITTER: Botonis OK 

PROVIDER: S-EPMC9205156 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature