Unknown

Dataset Information

0

Application of Machine Vision in Classifying Gait Frailty Among Older Adults.


ABSTRACT: Background: Frail older adults have an increased risk of adverse health outcomes and premature death. They also exhibit altered gait characteristics in comparison with healthy individuals. Methods: In this study, we created a Fried's frailty phenotype (FFP) labelled casual walking video set of older adults based on the West China Health and Aging Trend study. A series of hyperparameters in machine vision models were evaluated for body key point extraction (AlphaPose), silhouette segmentation (Pose2Seg, DPose2Seg, and Mask R-CNN), gait feature extraction (Gaitset, LGaitset, and DGaitset), and feature classification (AlexNet and VGG16), and were highly optimised during analysis of gait sequences of the current dataset. Results: The area under the curve (AUC) of the receiver operating characteristic (ROC) at the physical frailty state identification task for AlexNet was 0.851 (0.827-0.8747) and 0.901 (0.878-0.920) in macro and micro, respectively, and was 0.855 (0.834-0.877) and 0.905 (0.886-0.925) for VGG16 in macro and micro, respectively. Furthermore, this study presents the machine vision method equipped with better predictive performance globally than age and grip strength, as well as than 4-m-walking-time in healthy and pre-frailty classifying. Conclusion: The gait analysis method in this article is unreported and provides promising original tool for frailty and pre-frailty screening with the characteristics of convenience, objectivity, rapidity, and non-contact. These methods can be extended to any gait-related disease identification processes, as well as in-home health monitoring.

SUBMITTER: Liu Y 

PROVIDER: S-EPMC8637841 | biostudies-literature |

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC4308458 | biostudies-other
| S-EPMC7902408 | biostudies-literature
2022-02-16 | GSE196793 | GEO
| S-EPMC8705993 | biostudies-literature
| S-EPMC9951585 | biostudies-literature
| S-EPMC10457385 | biostudies-literature
| S-EPMC4768568 | biostudies-literature
| S-EPMC7648833 | biostudies-literature
| S-EPMC8190134 | biostudies-literature
| S-EPMC11232496 | biostudies-literature