Determination of Postural Control Mechanism in Overweight Adults Using The Artificial Neural Networks System and Nonlinear Autoregressive Moving Average Model

IF 0.8 Q4 ENGINEERING, BIOMEDICAL Advanced Biomedical Engineering Pub Date : 2020-01-01 DOI:10.14326/abe.9.154
T. Prasertsakul, W. Charoensuk
{"title":"Determination of Postural Control Mechanism in Overweight Adults Using The Artificial Neural Networks System and Nonlinear Autoregressive Moving Average Model","authors":"T. Prasertsakul, W. Charoensuk","doi":"10.14326/abe.9.154","DOIUrl":null,"url":null,"abstract":"Being overweight is one of several causes of balance impairment, and it increases the risk of falls. Balance assessments help diagnose this impairment. The outcomes from these assessments are not usually clear to investigate balance impairment in overweight adults. Several methods such as mathematical modeling can be used to investigate the postural control mechanisms in normal balance function. However, there is no study that is focused on the postural control mechanisms in overweight adults. This study aimed to de ne the postural control models underlying the application of the arti cial neural network (ANN) systems in normal weight and overweight populations. Ten participants were recruited and separated into two groups: normal weight (NW) and overweight (OW). There were two processes for determining the postural model in both groups. First, the optimal orders of the nonlinear autoregressive moving average (NARMA) model and the hidden nodes of the ANN system were identi ed. Mean square error (MSE), Akaike’s information criteria (AIC) and residual variance (RV) were used to identify these variables for both groups. Second, the coef cients of these models were de ned by the learned weights in the ANN system. The MSE, percent coef cient of variation (%CV), Kolmogorov-Smirnov (KS) test and maximal distance of cumulative distribution function (CDF) were de ned to evaluate the performance of the postural models. Furthermore, the orders of the NARMA model and relative importance were utilized to distinguish the postural control mechanisms between the two groups. During the training process, our results indicated that low MSE, AIC and RV were the criteria for hidden nodes and order selection in the NARMA model, which resulted in different patterns of postural models in each group. In the case of the testing process, the ndings revealed that the proposed technique could present different postural control strategies for each group. The ndings indicated that the postural control mechanism of NW subjects relied on the center of pressure (CoP) in the anterior-posterior (AP) direction, while body sway in the medio-lateral (ML) direction was vital to maintain equilibrium in the OW subjects. Accordingly, the proposed technique could be used to investigate the difference in postural control mechanism between the two groups.","PeriodicalId":54017,"journal":{"name":"Advanced Biomedical Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.8000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Biomedical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14326/abe.9.154","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0

Abstract

Being overweight is one of several causes of balance impairment, and it increases the risk of falls. Balance assessments help diagnose this impairment. The outcomes from these assessments are not usually clear to investigate balance impairment in overweight adults. Several methods such as mathematical modeling can be used to investigate the postural control mechanisms in normal balance function. However, there is no study that is focused on the postural control mechanisms in overweight adults. This study aimed to de ne the postural control models underlying the application of the arti cial neural network (ANN) systems in normal weight and overweight populations. Ten participants were recruited and separated into two groups: normal weight (NW) and overweight (OW). There were two processes for determining the postural model in both groups. First, the optimal orders of the nonlinear autoregressive moving average (NARMA) model and the hidden nodes of the ANN system were identi ed. Mean square error (MSE), Akaike’s information criteria (AIC) and residual variance (RV) were used to identify these variables for both groups. Second, the coef cients of these models were de ned by the learned weights in the ANN system. The MSE, percent coef cient of variation (%CV), Kolmogorov-Smirnov (KS) test and maximal distance of cumulative distribution function (CDF) were de ned to evaluate the performance of the postural models. Furthermore, the orders of the NARMA model and relative importance were utilized to distinguish the postural control mechanisms between the two groups. During the training process, our results indicated that low MSE, AIC and RV were the criteria for hidden nodes and order selection in the NARMA model, which resulted in different patterns of postural models in each group. In the case of the testing process, the ndings revealed that the proposed technique could present different postural control strategies for each group. The ndings indicated that the postural control mechanism of NW subjects relied on the center of pressure (CoP) in the anterior-posterior (AP) direction, while body sway in the medio-lateral (ML) direction was vital to maintain equilibrium in the OW subjects. Accordingly, the proposed technique could be used to investigate the difference in postural control mechanism between the two groups.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用人工神经网络系统和非线性自回归移动平均模型确定超重成人的姿势控制机制
超重是平衡能力受损的几个原因之一,它会增加跌倒的风险。平衡评估有助于诊断这种损伤。这些评估的结果通常不明确,以调查超重成年人的平衡障碍。利用数学建模等多种方法研究人体正常平衡时的体位控制机制。然而,目前还没有针对超重成人的姿势控制机制的研究。本研究旨在建立基于人工神经网络(ANN)系统在正常体重和超重人群中的应用的姿势控制模型。10名参与者被分为正常体重组(NW)和超重组(OW)。两组的体位模型有两个确定过程。首先,确定非线性自回归移动平均(NARMA)模型的最优阶数和神经网络系统的隐节点,并利用均方误差(MSE)、赤池信息准则(AIC)和残差方差(RV)对两组变量进行识别。其次,利用人工神经网络系统中学习到的权值来确定模型的系数;采用MSE、百分比变异系数(%CV)、Kolmogorov-Smirnov (KS)检验和累积分布函数的最大距离(CDF)来评价姿态模型的性能。此外,利用NARMA模型的顺序和相对重要性来区分两组之间的姿势控制机制。在训练过程中,我们的研究结果表明,低MSE、AIC和RV是NARMA模型中隐藏节点和顺序选择的标准,这导致了各组姿势模型的不同模式。在测试过程中,研究结果表明,所提出的技术可以为每个组提供不同的姿势控制策略。研究结果表明,NW受试者的体位控制机制依赖于前后方向的压力中心(CoP),而OW受试者的中外侧(ML)方向的身体摇摆对维持平衡至关重要。因此,该技术可用于研究两组之间姿势控制机制的差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Advanced Biomedical Engineering
Advanced Biomedical Engineering ENGINEERING, BIOMEDICAL-
CiteScore
1.40
自引率
10.00%
发文量
15
审稿时长
15 weeks
期刊最新文献
SATORI: Amplification-free digital RNA detection method for the diagnosis of viral infections. Basic Investigation of the Effect of Insole Shape on Leg Skeletal Muscle Mass and Pressure Changes during Walking Investigating Mental Task Combination for Brain-Computer Interface Based on Brain State Discrimination Using Subjective Ratings Development of Beat Analysis Software for the Non-invasive Evaluation of Cardiac Constructs Fabricated with a Three-dimensional Bioprinter Transepidermal Water Loss Estimation Model for Evaluating Skin Barrier Function
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1