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Machine vision-based gait scan method for identifying cognitive impairment in older adults.
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![loading](/sites/all/modules/hf_eds/images/loading.gif)
- Author(s): Yuzhen Qin; Haowei Zhang; Linbo Qing; Qinghua Liu; Hua Jiang; Shen Xu; Yixin Liu; Xiaohai He
- Source:
Frontiers in Aging Neuroscience; 2024, p1-12, 12p- Subject Terms:
COGNITION disorders diagnosis; CROSS-sectional method; MENTAL health surveys; RECEIVER operating characteristic curves; RESEARCH funding; QUESTIONNAIRES; DIAGNOSIS; GAIT in humans; DESCRIPTIVE statistics; MACHINE learning; DATA analysis software; CONFIDENCE intervals; ALGORITHMS; VIDEO recording; OLD age - Source:
- Additional Information
- Subject Terms:
- Abstract: Objective: Early identification of cognitive impairment in older adults could reduce the burden of age-related disabilities. Gait parameters are associated with and predictive of cognitive decline. Although a variety of sensors and machine learning analysis methods have been used in cognitive studies, a deep optimized machine vision-based method for analyzing gait to identify cognitive decline is needed. Methods: This study used a walking footage dataset of 158 adults named West China Hospital Elderly Gait, which was labelled by performance on the Short Portable Mental Status Questionnaire. We proposed a novel recognition network, Deep Optimized GaitPart (DO-GaitPart), based on silhouette and skeleton gait images. Three improvements were applied: short-term temporal template generator (STTG) in the template generation stage to decrease computational cost and minimize loss of temporal information; depth-wise spatial feature extractor (DSFE) to extract both global and local fine-grained spatial features from gait images; and multi-scale temporal aggregation (MTA), a temporal modeling method based on attention mechanism, to improve the distinguishability of gait patterns. Results: An ablation test showed that each component of DO-GaitPart was essential. DO-GaitPart excels in backpack walking scene on CASIA-B dataset, outperforming comparison methods, which were GaitSet, GaitPart, MT3D, 3D Local, TransGait, CSTL, GLN, GaitGL and SMPLGait on Gait3D dataset. The proposed machine vision gait feature identification method achieved a receiver operating characteristic/area under the curve (ROCAUC) of 0.876 (0.852-0.900) on the cognitive state classification task. Conclusion: The proposed method performed well identifying cognitive decline from the gait video datasets, making it a prospective prototype tool in cognitive assessment. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Frontiers in Aging Neuroscience is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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