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=Volume 13 - 2021

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 optimized 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 a 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.