PDF(Pubmed)
Abstract：
UNASSIGNED: The aging process is associated with a cognitive and physical declines that affects neuromotor control, memory, executive functions, and motor abilities. Previous studies have made efforts to find biomarkers, utilizing complex factors such as gait as indicators of cognitive and physical health in older adults. However, while gait involves various complex factors, such as attention and the integration of sensory input, cognitive-related motor planning and execution, and the musculoskeletal system, research on biomarkers that simultaneously considers multiple factors is scarce. This study aimed to extract gait features through stepwise regression, based on three speeds, and evaluate the accuracy of machine-learning (ML) models based on the selected features to solve classification problems caused by declines in cognitive function (Cog) and physical function (PF), and in Cog and muscle strength (MS).
UNASSIGNED: Cognitive assessments, five times sit-to-stand, and handgrip strength were performed to evaluate the Cog, PF, and MS of 198 women aged 65 years or older. For gait assessment, all participants walked along a 19-meter straight path at three speeds [preferred walking speed (PWS), slower walking speed (SWS), and faster walking speed (FWS)]. The extracted gait features based on the three speeds were selected using stepwise regression.
UNASSIGNED: The ML model accuracies were revealed as follows: 91.2% for the random forest model when using all gait features and 91.9% when using the three features (walking speed and coefficient of variation of the left double support phase at FWS and the right double support phase at SWS) selected for the Cog+PF+ and Cog-PF- classification. In addition, support vector machine showed a Cog+MS+ and Cog-MS- classification problem with 93.6% accuracy when using all gait features and two selected features (left step time at PWS and gait asymmetry at SWS).
UNASSIGNED: Our study provides insights into the gait characteristics of older women with decreased Cog, PF, and MS, based on the three walking speeds and ML analysis using selected gait features, and may help improve objective classification and evaluation according to declines in Cog, PF, and MS among older women.
UNASSIGNED: Cognitive assessments, five times sit-to-stand, and handgrip strength were performed to evaluate the Cog, PF, and MS of 198 women aged 65 years or older. For gait assessment, all participants walked along a 19-meter straight path at three speeds [preferred walking speed (PWS), slower walking speed (SWS), and faster walking speed (FWS)]. The extracted gait features based on the three speeds were selected using stepwise regression.
UNASSIGNED: The ML model accuracies were revealed as follows: 91.2% for the random forest model when using all gait features and 91.9% when using the three features (walking speed and coefficient of variation of the left double support phase at FWS and the right double support phase at SWS) selected for the Cog+PF+ and Cog-PF- classification. In addition, support vector machine showed a Cog+MS+ and Cog-MS- classification problem with 93.6% accuracy when using all gait features and two selected features (left step time at PWS and gait asymmetry at SWS).
UNASSIGNED: Our study provides insights into the gait characteristics of older women with decreased Cog, PF, and MS, based on the three walking speeds and ML analysis using selected gait features, and may help improve objective classification and evaluation according to declines in Cog, PF, and MS among older women.
摘要：
■衰老过程与影响神经运动控制的认知和身体衰退有关，记忆,执行功能，和运动能力。以前的研究已经努力寻找生物标志物，利用步态等复杂因素作为老年人认知和身体健康的指标。然而,虽然步态涉及各种复杂因素，例如注意力和感觉输入的整合，与认知相关的运动计划和执行，和肌肉骨骼系统，同时考虑多种因素的生物标志物研究很少。本研究旨在通过逐步回归提取步态特征，基于三种速度，并根据选定的特征评估机器学习（ML）模型的准确性，以解决由认知功能（Cog）和身体功能（PF）下降引起的分类问题，以及Cog和肌肉力量（MS）。
■认知评估，五次坐着站着，和手握强度进行评估，PF,和198名65岁或以上的女性的MS。对于步态评估，所有参与者以三种速度沿着19米的直线路径行走[首选步行速度（PWS），较慢的步行速度（SWS），和更快的步行速度（FWS）]。使用逐步回归选择基于三个速度的提取的步态特征。
■ML模型的准确性显示如下：使用所有步态特征时，随机森林模型为91.2％，使用三个特征（步行速度和变异系数）时，为91.9％选择了CogPF和Cog-PF-分类。此外,支持向量机在使用所有步态特征和两个选定特征（PWS时的左步时间和SWS时的步态不对称）时，显示出CogMS和Cog-MS分类问题，准确率为93.6％。
■我们的研究提供了对老年女性的步态特征的见解，PF,MS,基于三种步行速度和使用选定步态特征的ML分析，并可能有助于根据Cog的下降改善客观分类和评估，PF,和老年女性的MS。
■认知评估，五次坐着站着，和手握强度进行评估，PF,和198名65岁或以上的女性的MS。对于步态评估，所有参与者以三种速度沿着19米的直线路径行走[首选步行速度（PWS），较慢的步行速度（SWS），和更快的步行速度（FWS）]。使用逐步回归选择基于三个速度的提取的步态特征。
■ML模型的准确性显示如下：使用所有步态特征时，随机森林模型为91.2％，使用三个特征（步行速度和变异系数）时，为91.9％选择了CogPF和Cog-PF-分类。此外,支持向量机在使用所有步态特征和两个选定特征（PWS时的左步时间和SWS时的步态不对称）时，显示出CogMS和Cog-MS分类问题，准确率为93.6％。
■我们的研究提供了对老年女性的步态特征的见解，PF,MS,基于三种步行速度和使用选定步态特征的ML分析，并可能有助于根据Cog的下降改善客观分类和评估，PF,和老年女性的MS。
