Depth-Imaging for Gait Analysis on a Treadmill in Older Adults at Risk of Falling

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL IEEE Journal of Translational Engineering in Health and Medicine-Jtehm Pub Date : 2023-03-19 DOI:10.1109/JTEHM.2023.3277890
Michel Hackbarth;Jessica Koschate;Sandra Lau;Tania Zieschang
{"title":"Depth-Imaging for Gait Analysis on a Treadmill in Older Adults at Risk of Falling","authors":"Michel Hackbarth;Jessica Koschate;Sandra Lau;Tania Zieschang","doi":"10.1109/JTEHM.2023.3277890","DOIUrl":null,"url":null,"abstract":"Background: Accidental falls are a major health issue in older people. One significant and potentially modifiable risk factor is reduced gait stability. Clinicians do not have sophisticated kinematic options to measure this risk factor with simple and affordable systems. Depth-imaging with AI-pose estimation can be used for gait analysis in young healthy adults. However, is it applicable for measuring gait in older adults at a risk of falling? Methods: In this methodological comparison 59 older adults with and without a history of falls walked on a treadmill while their gait pattern was recorded with multiple inertial measurement units and with an Azure Kinect depth-camera. Spatiotemporal gait parameters of both systems were compared for convergent validity and with a Bland-Altman plot. Results: Correlation between systems for stride length (r=.992, \n<inline-formula> <tex-math>$\\text{p} &lt; 0.001$ </tex-math></inline-formula>\n) and stride time (r=0.914, \n<inline-formula> <tex-math>$\\text{p} &lt; 0.001$ </tex-math></inline-formula>\n) was high. Bland-Altman plots revealed a moderate agreement in stride length (−0.74 ± 3.68 cm; [−7.96 cm to 6.47 cm]) and stride time (−3.7±54 ms; [−109 ms to 102 ms]). Conclusion: Gait parameters in older adults with and without a history of falls can be measured with inertial measurement units and Azure Kinect cameras. Affordable and small depth-cameras agree with IMUs for gait analysis in older adults with and without an increased risk of falling. However, tolerable accuracy is limited to the average over multiple steps of spatiotemporal parameters derived from the initial foot contact. Clinical Translation Statement— Gait parameters in older adults with and without a history of falls can be measured with inertial measurement units and Azure Kinect. Affordable and small depth-cameras, developed for various purposes in research and industry, agree with IMUs in clinical gait analysis in older adults with and without an increased risk of falling. However, tolerable accuracy to assess function or monitor changes in gait is limited to the average over multiple steps of spatiotemporal parameters derived from the initial foot contact.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"11 ","pages":"479-486"},"PeriodicalIF":3.7000,"publicationDate":"2023-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10129931","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10129931/","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Accidental falls are a major health issue in older people. One significant and potentially modifiable risk factor is reduced gait stability. Clinicians do not have sophisticated kinematic options to measure this risk factor with simple and affordable systems. Depth-imaging with AI-pose estimation can be used for gait analysis in young healthy adults. However, is it applicable for measuring gait in older adults at a risk of falling? Methods: In this methodological comparison 59 older adults with and without a history of falls walked on a treadmill while their gait pattern was recorded with multiple inertial measurement units and with an Azure Kinect depth-camera. Spatiotemporal gait parameters of both systems were compared for convergent validity and with a Bland-Altman plot. Results: Correlation between systems for stride length (r=.992, $\text{p} < 0.001$ ) and stride time (r=0.914, $\text{p} < 0.001$ ) was high. Bland-Altman plots revealed a moderate agreement in stride length (−0.74 ± 3.68 cm; [−7.96 cm to 6.47 cm]) and stride time (−3.7±54 ms; [−109 ms to 102 ms]). Conclusion: Gait parameters in older adults with and without a history of falls can be measured with inertial measurement units and Azure Kinect cameras. Affordable and small depth-cameras agree with IMUs for gait analysis in older adults with and without an increased risk of falling. However, tolerable accuracy is limited to the average over multiple steps of spatiotemporal parameters derived from the initial foot contact. Clinical Translation Statement— Gait parameters in older adults with and without a history of falls can be measured with inertial measurement units and Azure Kinect. Affordable and small depth-cameras, developed for various purposes in research and industry, agree with IMUs in clinical gait analysis in older adults with and without an increased risk of falling. However, tolerable accuracy to assess function or monitor changes in gait is limited to the average over multiple steps of spatiotemporal parameters derived from the initial foot contact.

Abstract Image

Abstract Image

Abstract Image

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
有跌倒风险的老年人跑步机步态分析的深度成像。
背景:意外跌倒是老年人的主要健康问题。步态稳定性降低是一个重要且可能改变的风险因素。临床医生没有复杂的运动学选择来用简单且负担得起的系统来测量这种风险因素。具有AI姿态估计的深度成像可用于年轻健康成年人的步态分析。然而,它适用于测量有跌倒风险的老年人的步态吗?方法:在这项方法学比较中,59名有和没有跌倒史的老年人在跑步机上行走,同时用多个惯性测量单元和Azure Kinect深度相机记录他们的步态模式。比较了两个系统的时空步态参数的收敛有效性和Bland-Altman图。结果:步幅长度(r=.992,[公式:见正文])和步幅时间(r=0.914,[公式,见正文]])系统之间的相关性很高。Bland-Altman图显示步幅长度(-0.74±3.68厘米;[7.96厘米至6.47厘米])和步幅时间(-3.7±54毫秒;[-109毫秒至102毫秒])适度一致。结论:有和没有跌倒史的老年人的步态参数可以用惯性测量装置和Azure Kinect相机进行测量。价格合理的小型深度相机与IMU一致,用于对跌倒风险增加和不增加的老年人进行步态分析。然而,可容忍的精度被限制为从初始脚接触导出的时空参数的多个步骤上的平均值。临床翻译声明-有和没有跌倒史的老年人的步态参数可以使用惯性测量单元和Azure Kinect进行测量。为研究和工业的各种目的开发的价格合理的小型深度相机,在跌倒风险增加和不增加的老年人的临床步态分析中与IMU一致。然而,评估功能或监测步态变化的可容忍精度仅限于从初始足部接触导出的时空参数的多个步骤的平均值。
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
CiteScore
7.40
自引率
2.90%
发文量
65
审稿时长
27 weeks
期刊介绍: The IEEE Journal of Translational Engineering in Health and Medicine is an open access product that bridges the engineering and clinical worlds, focusing on detailed descriptions of advanced technical solutions to a clinical need along with clinical results and healthcare relevance. The journal provides a platform for state-of-the-art technology directions in the interdisciplinary field of biomedical engineering, embracing engineering, life sciences and medicine. A unique aspect of the journal is its ability to foster a collaboration between physicians and engineers for presenting broad and compelling real world technological and engineering solutions that can be implemented in the interest of improving quality of patient care and treatment outcomes, thereby reducing costs and improving efficiency. The journal provides an active forum for clinical research and relevant state-of the-art technology for members of all the IEEE societies that have an interest in biomedical engineering as well as reaching out directly to physicians and the medical community through the American Medical Association (AMA) and other clinical societies. The scope of the journal includes, but is not limited, to topics on: Medical devices, healthcare delivery systems, global healthcare initiatives, and ICT based services; Technological relevance to healthcare cost reduction; Technology affecting healthcare management, decision-making, and policy; Advanced technical work that is applied to solving specific clinical needs.
期刊最新文献
Elevating Patient Care With Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients Non-Contact Monitoring of Inhalation-Exhalation (I:E) Ratio in Non-Ventilated Subjects A Multi-Task Based Deep Learning Framework With Landmark Detection for MRI Couinaud Segmentation Video-Based Respiratory Rate Estimation for Infants in the NICU A Novel Chest-Based PPG Measurement System
×
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