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Liu Y, He X, Wang R, Teng Q, Hu R, Qing L, Wang Z, He X, Yin B, Mou Y, Du Y, Li X, Wang H, Liu X, Zhou L, Deng L, Xu Z, Xiao C, Ge M, Sun X, Jiang J, Chen J, Lin X, Xia L, Gong H, Yu H, Dong B. Application of Machine Vision in Classifying Gait Frailty Among Older Adults. Front Aging Neurosci 2021; 13:757823. [PMID: 34867286 PMCID: PMC8637841 DOI: 10.3389/fnagi.2021.757823] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 10/18/2021] [Indexed: 02/05/2023] Open
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.
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Affiliation(s)
- Yixin Liu
- National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
- Geriatric Health Care and Medical Research Center, Sichuan University, Chengdu, China
- Department of Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaohai He
- College of Electronics and Information Engineering, Sichuan University, Chengdu, China
| | - Renjie Wang
- Department of Rehabilitation Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Qizhi Teng
- College of Electronics and Information Engineering, Sichuan University, Chengdu, China
| | - Rui Hu
- College of Electronics and Information Engineering, Sichuan University, Chengdu, China
| | - Linbo Qing
- College of Electronics and Information Engineering, Sichuan University, Chengdu, China
| | - Zhengyong Wang
- College of Electronics and Information Engineering, Sichuan University, Chengdu, China
| | - Xuan He
- College of Electronics and Information Engineering, Sichuan University, Chengdu, China
| | - Biao Yin
- College of Electronics and Information Engineering, Sichuan University, Chengdu, China
| | - Yi Mou
- Geroscience and Chronic Disease Department, The 8th Municipal Hospital for the People, Chengdu, China
| | - Yanping Du
- Department of Rehabilitation Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Xinyi Li
- Medical Examination Center, Aviation Industry Corporation of China 363 Hospital, Chengdu, China
| | - Hui Wang
- National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
- Geriatric Health Care and Medical Research Center, Sichuan University, Chengdu, China
- Department of Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaolei Liu
- National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
- Geriatric Health Care and Medical Research Center, Sichuan University, Chengdu, China
- Department of Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Lixing Zhou
- National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
- Geriatric Health Care and Medical Research Center, Sichuan University, Chengdu, China
- Department of Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Linghui Deng
- National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
- Geriatric Health Care and Medical Research Center, Sichuan University, Chengdu, China
- Department of Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Ziqi Xu
- West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, China
| | - Chun Xiao
- National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Meiling Ge
- National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
- Geriatric Health Care and Medical Research Center, Sichuan University, Chengdu, China
- Department of Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Xuelian Sun
- National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
- Geriatric Health Care and Medical Research Center, Sichuan University, Chengdu, China
- Department of Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Junshan Jiang
- Medical College, Jiangsu University, Zhenjiang, China
| | - Jiaoyang Chen
- Public Health Department, Chengdu Medical College, Chengdu, China
| | - Xinyi Lin
- Public Health Department, Chengdu Medical College, Chengdu, China
| | - Ling Xia
- Public Health Department, Chengdu Medical College, Chengdu, China
| | - Haoran Gong
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Haopeng Yu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Birong Dong
- National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
- Geriatric Health Care and Medical Research Center, Sichuan University, Chengdu, China
- Department of Geriatrics, West China Hospital, Sichuan University, Chengdu, China
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Macoveciuc I, Rando CJ, Borrion H. Forensic Gait Analysis and Recognition: Standards of Evidence Admissibility. J Forensic Sci 2019; 64:1294-1303. [PMID: 30791120 DOI: 10.1111/1556-4029.14036] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 01/21/2019] [Accepted: 01/21/2019] [Indexed: 11/30/2022]
Abstract
Gait is one biological characteristic which has attracted strong research interest due to its potential use in human identification. Although almost two decades have passed since a forensic gait expert has testified to the identity of a perpetrator in court, the methods remain insufficiently robust, considering the recent paradigm shift witnessed in the forensic science community regarding quality of evidence. In contrast, technological advancements have taken the lead, and research into automated gait recognition has greatly surpassed forensic gait analysis in terms of the size of acquired datasets and demographic variability of participants, tested variables, and statistical evaluation of results. Despite these advantages, gait recognition presents with different problems which are yet to be resolved. Therefore, courts should treat gait evidence with caution, as they should any other form of evidence originating from disciplines without fully established codes of practice, error rates, and demonstrable applications in forensic scenarios.
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Affiliation(s)
- Ioana Macoveciuc
- Department of Security and Crime Science, University College London, 35 Tavistock Square, London, WC1H 9EZ, U.K.,Institute of Archaeology, University College London, 31-34 Gordon Square, London, WC1H 0PY, U.K
| | - Carolyn J Rando
- Department of Security and Crime Science, University College London, 35 Tavistock Square, London, WC1H 9EZ, U.K
| | - Hervé Borrion
- Institute of Archaeology, University College London, 31-34 Gordon Square, London, WC1H 0PY, U.K
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