An Automatic Gait Analysis Pipeline for Wearable Sensors: A Pilot Study in Parkinson's Disease.

ABSTRACT: The use of wearable sensors allows continuous recordings of physical activity from participants in free-living or at-home clinical studies. The large amount of data collected demands automatic analysis pipelines to extract gait parameters that can be used as clinical endpoints. We introduce a deep learning-based automatic pipeline for wearables that processes tri-axial accelerometry data and extracts gait events-bout segmentation, initial contact (IC), and final contact (FC)-from a single sensor located at either the lower back (near L5), shin or wrist. The gait events detected are posteriorly used for gait parameter estimation, such as step time, length, and symmetry. We report results from a leave-one-subject-out (LOSO) validation on a pilot study dataset of five participants clinically

SUBMITTER: Peraza LR 

PROVIDER: S-EPMC8707484 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature