AUTHOR=Liu Yixin , He Xiaohai , Wang Renjie , Teng Qizhi , Hu Rui , Qing Linbo , Wang Zhengyong , He Xuan , Yin Biao , Mou Yi , Du Yanping , Li Xinyi , Wang Hui , Liu Xiaolei , Zhou Lixing , Deng Linghui , Xu Ziqi , Xiao Chun , Ge Meiling , Sun Xuelian , Jiang Junshan , Chen Jiaoyang , Lin Xinyi , Xia Ling , Gong Haoran , Yu Haopeng , Dong Birong
TITLE=Application of Machine Vision in Classifying Gait Frailty Among Older Adults
JOURNAL=Frontiers in Aging Neuroscience
VOLUME=13
YEAR=2021
URL=https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2021.757823
DOI=10.3389/fnagi.2021.757823
ISSN=1663-4365
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.