Deep Neural Networks for Human's Fall-risk Prediction using Force-Plate Time Series Signal.

ABSTRACT: Early and accurate identification of the balance deficits could reduce falls, in particular for older adults, a prone population. Our work investigates deep neural networks' capacity to identify human balance patterns towards predicting fall-risk. Human balance ability can be characterized based on commonly-used balance metrics, such as those derived from the force-plate time series. We hypothesized that low, moderate, and high risk of falling can be characterized based on balance metrics, derived from the force-plate time series, in conjunction with deep learning algorithms. Further, we predicted that our proposed One-One-One Deep Neural Networks algorithm provides a considerable increase in performance compared to other algorithms. Here, an open source force-plate dataset, which quantifi

SUBMITTER: Savadkoohi M 

PROVIDER: S-EPMC9540455 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature