Causal analysis of inertial body sensors for enhancing gait assessment separability towards multiple sclerosis diagnosis

Jiaqi Gong, J. Lach, Yanjun Qi, M. Goldman
{"title":"Causal analysis of inertial body sensors for enhancing gait assessment separability towards multiple sclerosis diagnosis","authors":"Jiaqi Gong, J. Lach, Yanjun Qi, M. Goldman","doi":"10.1109/BSN.2015.7299400","DOIUrl":null,"url":null,"abstract":"Gait assessment is a common method for diagnosing various diseases, disorders, and injuries, studying their impact on mobility, and evaluating the efficacy of various therapeutic interventions. The recent emergence of inertial body sensors for gait assessment addresses the limitations of visual observation and subjective clinical evaluation by providing more precise and objective measures. Inertial sensors have been included in an ongoing study at the University of Virginia Medical Center on Multiple Sclerosis (MS), a chronic autoimmune disorder of the central nervous system (CNS) that produces neurologic impairment and functional disability over time, with the goal of improving the ability to assess MS-affected gait and to distinguish between subjects with MS and those without MS. This work presents a gait assessment technique based on causal modeling to distinguish MS-affected gait and healthy gait. The approach in this work is based on the hypothesis that the strength of interaction between body parts during walking is greater in healthy controls that in MS subjects. The strength of interaction was quantified using a causality index based on the pairwise causal relationships between body parts as characterized by the Phase Slope Index (PSI) of inertial signals from pairs of body parts. In a pilot study with 41 subjects (28 MS subjects and 13 healthy controls), the approach developed in this paper provided better separability (p <; 0.0001) compared with existing methods.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BSN.2015.7299400","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 17

Abstract

Gait assessment is a common method for diagnosing various diseases, disorders, and injuries, studying their impact on mobility, and evaluating the efficacy of various therapeutic interventions. The recent emergence of inertial body sensors for gait assessment addresses the limitations of visual observation and subjective clinical evaluation by providing more precise and objective measures. Inertial sensors have been included in an ongoing study at the University of Virginia Medical Center on Multiple Sclerosis (MS), a chronic autoimmune disorder of the central nervous system (CNS) that produces neurologic impairment and functional disability over time, with the goal of improving the ability to assess MS-affected gait and to distinguish between subjects with MS and those without MS. This work presents a gait assessment technique based on causal modeling to distinguish MS-affected gait and healthy gait. The approach in this work is based on the hypothesis that the strength of interaction between body parts during walking is greater in healthy controls that in MS subjects. The strength of interaction was quantified using a causality index based on the pairwise causal relationships between body parts as characterized by the Phase Slope Index (PSI) of inertial signals from pairs of body parts. In a pilot study with 41 subjects (28 MS subjects and 13 healthy controls), the approach developed in this paper provided better separability (p <; 0.0001) compared with existing methods.
惯性体传感器在多发性硬化症诊断中增强步态评估可分离性的原因分析
步态评估是诊断各种疾病、障碍和损伤、研究其对活动能力的影响以及评估各种治疗干预措施疗效的常用方法。最近出现的用于步态评估的惯性身体传感器通过提供更精确和客观的测量,解决了视觉观察和主观临床评估的局限性。惯性传感器已被纳入弗吉尼亚大学医学中心正在进行的多发性硬化症(MS)研究,多发性硬化症是一种中枢神经系统(CNS)的慢性自身免疫性疾病,随着时间的推移会导致神经损伤和功能残疾。为了提高对MS影响步态的评估能力和区分MS患者和非MS患者的能力,本研究提出了一种基于因果模型的步态评估技术来区分MS影响步态和健康步态。这项工作的方法是基于这样一种假设,即健康对照者在行走过程中身体部位之间的相互作用强度大于多发性硬化症受试者。相互作用的强度使用基于身体部位之间的成对因果关系的因果指数来量化,该因果关系以来自成对身体部位的惯性信号的相位斜率指数(PSI)为特征。在一项41名受试者(28名多发性硬化症受试者和13名健康对照)的初步研究中,本文开发的方法具有更好的可分离性(p <;0.0001)。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信