■早期识别老年人的认知障碍可以减轻与年龄相关的残疾的负担。步态参数与认知衰退相关并可预测认知衰退。尽管在认知研究中已经使用了多种传感器和机器学习分析方法,需要一种深度优化的基于机器视觉的步态分析方法来识别认知衰退.
■这项研究使用了158名名为华西医院老年人步态的成年人的步行录像数据集,在简短的便携式精神状态问卷上被标记为表现。我们提出了一种新颖的识别网络,深度优化GaitPart(DO-GaitPart),基于轮廓和骨骼步态图像。应用了三个改进:在模板生成阶段使用短期时间模板生成器(STTG),以降低计算成本并最大程度地减少时间信息的损失;深度空间特征提取器(DSFE)从步态图像中提取全局和局部细粒度空间特征;以及多尺度时间聚合(MTA),一种基于注意力机制的时间建模方法,以提高步态模式的可分辨性。
■消融测试表明,DO-GaitPart的每个组件都是必不可少的。DO-GaitPart在CASIA-B数据集上的背包行走场景中表现出色,优于比较方法,它们是GaitSet,GaitPart,MT3D,3D本地,TransGait,CSTL,GLN,Gait3D数据集上的GaitGL和SMPLGait。提出的机器视觉步态特征识别方法在认知状态分类任务上实现了0.876(0.852-0.900)的接收机工作特征/曲线下面积(ROCAUC)。
■所提出的方法从步态视频数据集中很好地识别了认知衰退,使其成为认知评估中的前瞻性原型工具。
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: The proposed method performed well identifying cognitive decline from the gait video datasets, making it a prospective prototype tool in cognitive assessment.