Machine vision-based gait scan method for identifying cognitive impairment in older adults

被引:0
|
作者
Qin, Yuzhen [1 ]
Zhang, Haowei [2 ]
Qing, Linbo [1 ]
Liu, Qinghua [1 ]
Jiang, Hua [3 ,4 ]
Xu, Shen [5 ]
Liu, Yixin [6 ,7 ]
He, Xiaohai [1 ]
机构
[1] Sichuan Univ, Coll Elect & Informat Engn, Chengdu, Peoples R China
[2] Sichuan Univ, West China Sch Med, Chengdu, Peoples R China
[3] Chengdu Univ, Clin Med Coll, Dept Geriatr, Chengdu, Peoples R China
[4] Chengdu Univ, Affiliated Hosp, Chengdu, Peoples R China
[5] Sichuan Univ, West China Hosp, Natl Clin Res Ctr Geriatr, Dept Endocrinol & Metab, Chengdu, Peoples R China
[6] Sichuan Univ, West China Hosp, Natl Clin Res Ctr Geriatr, Dept Geriatr, Chengdu, Peoples R China
[7] Sichuan Univ, Geriatr Hlth Care & Med Res Ctr, Chengdu, Peoples R China
来源
FRONTIERS IN AGING NEUROSCIENCE | 2024年 / 16卷
关键词
gait; gait recognition; cognitive impairment; machine vision; CNN; BiLSTM; DEMENTIA; WALKING; CARE;
D O I
10.3389/fnagi.2024.1341227
中图分类号
R592 [老年病学]; C [社会科学总论];
学科分类号
03 ; 0303 ; 100203 ;
摘要
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.
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页数:12
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