Application of Machine Vision in Classifying Gait Frailty Among Older Adults
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...
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Frontiers Media S.A.
2021
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oai:doaj.org-article:c0a946e4167348d7a1c06c176bb6f8472021-11-16T15:53:14ZApplication of Machine Vision in Classifying Gait Frailty Among Older Adults1663-436510.3389/fnagi.2021.757823https://doaj.org/article/c0a946e4167348d7a1c06c176bb6f8472021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnagi.2021.757823/fullhttps://doaj.org/toc/1663-4365Background: 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.Yixin LiuYixin LiuYixin LiuXiaohai HeRenjie WangQizhi TengRui HuLinbo QingZhengyong WangXuan HeBiao YinYi MouYanping DuXinyi LiHui WangHui WangHui WangXiaolei LiuXiaolei LiuXiaolei LiuLixing ZhouLixing ZhouLixing ZhouLinghui DengLinghui DengLinghui DengZiqi XuChun XiaoMeiling GeMeiling GeMeiling GeXuelian SunXuelian SunXuelian SunJunshan JiangJiaoyang ChenXinyi LinLing XiaHaoran GongHaopeng YuHaopeng YuBirong DongBirong DongBirong DongFrontiers Media S.A.articlefrailtygaitmachine visionbiomarkerspreventative health carefeature extractionNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Aging Neuroscience, Vol 13 (2021) |
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frailty gait machine vision biomarkers preventative health care feature extraction Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 |
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frailty gait machine vision biomarkers preventative health care feature extraction Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Yixin Liu Yixin Liu Yixin Liu Xiaohai He Renjie Wang Qizhi Teng Rui Hu Linbo Qing Zhengyong Wang Xuan He Biao Yin Yi Mou Yanping Du Xinyi Li Hui Wang Hui Wang Hui Wang Xiaolei Liu Xiaolei Liu Xiaolei Liu Lixing Zhou Lixing Zhou Lixing Zhou Linghui Deng Linghui Deng Linghui Deng Ziqi Xu Chun Xiao Meiling Ge Meiling Ge Meiling Ge Xuelian Sun Xuelian Sun Xuelian Sun Junshan Jiang Jiaoyang Chen Xinyi Lin Ling Xia Haoran Gong Haopeng Yu Haopeng Yu Birong Dong Birong Dong Birong Dong Application of Machine Vision in Classifying Gait Frailty Among Older Adults |
| description |
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. |
| format |
article |
| author |
Yixin Liu Yixin Liu Yixin Liu Xiaohai He Renjie Wang Qizhi Teng Rui Hu Linbo Qing Zhengyong Wang Xuan He Biao Yin Yi Mou Yanping Du Xinyi Li Hui Wang Hui Wang Hui Wang Xiaolei Liu Xiaolei Liu Xiaolei Liu Lixing Zhou Lixing Zhou Lixing Zhou Linghui Deng Linghui Deng Linghui Deng Ziqi Xu Chun Xiao Meiling Ge Meiling Ge Meiling Ge Xuelian Sun Xuelian Sun Xuelian Sun Junshan Jiang Jiaoyang Chen Xinyi Lin Ling Xia Haoran Gong Haopeng Yu Haopeng Yu Birong Dong Birong Dong Birong Dong |
| author_facet |
Yixin Liu Yixin Liu Yixin Liu Xiaohai He Renjie Wang Qizhi Teng Rui Hu Linbo Qing Zhengyong Wang Xuan He Biao Yin Yi Mou Yanping Du Xinyi Li Hui Wang Hui Wang Hui Wang Xiaolei Liu Xiaolei Liu Xiaolei Liu Lixing Zhou Lixing Zhou Lixing Zhou Linghui Deng Linghui Deng Linghui Deng Ziqi Xu Chun Xiao Meiling Ge Meiling Ge Meiling Ge Xuelian Sun Xuelian Sun Xuelian Sun Junshan Jiang Jiaoyang Chen Xinyi Lin Ling Xia Haoran Gong Haopeng Yu Haopeng Yu Birong Dong Birong Dong Birong Dong |
| author_sort |
Yixin Liu |
| title |
Application of Machine Vision in Classifying Gait Frailty Among Older Adults |
| title_short |
Application of Machine Vision in Classifying Gait Frailty Among Older Adults |
| title_full |
Application of Machine Vision in Classifying Gait Frailty Among Older Adults |
| title_fullStr |
Application of Machine Vision in Classifying Gait Frailty Among Older Adults |
| title_full_unstemmed |
Application of Machine Vision in Classifying Gait Frailty Among Older Adults |
| title_sort |
application of machine vision in classifying gait frailty among older adults |
| publisher |
Frontiers Media S.A. |
| publishDate |
2021 |
| url |
https://doaj.org/article/c0a946e4167348d7a1c06c176bb6f847 |
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