Hidden Markov Model based stride segmentation on unsupervised free-living gait data in Parkinson's disease patients.

ABSTRACT:

Background

To objectively assess a patient's gait, a robust identification of stride borders is one of the first steps in inertial sensor-based mobile gait analysis pipelines. While many different methods for stride segmentation have been presented in the literature, an out-of-lab evaluation of respective algorithms on free-living gait is still missing.

Method

To address this issue, we present a comprehensive free-living evaluation dataset, including 146.574 semi-automatic labeled strides of 28 Parkinson's Disease patients. This dataset was used to evaluate the segmentation performance of a new Hidden Markov Model (HMM) based stride segmentation approach compared to an available dynamic time warping (DTW) based method.

Results

The proposed HMM achieved a mean F1-score

SUBMITTER: Roth N 

PROVIDER: S-EPMC8173987 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature