A novel in-bed body posture monitoring for decubitus ulcer prevention using body pressure distribution mapping.

IF 2.9 4区 医学 Q3 ENGINEERING, BIOMEDICAL
Lindsay Stern, Geoff Fernie, Atena Roshan Fekr
{"title":"A novel in-bed body posture monitoring for decubitus ulcer prevention using body pressure distribution mapping.","authors":"Lindsay Stern, Geoff Fernie, Atena Roshan Fekr","doi":"10.1186/s12938-024-01227-x","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Decubitus ulcers are prevalent among the aging population due to a gradual decline in their overall health, such as nutrition, mental health, and mobility, resulting in injury to the skin and tissue. The most common technique to prevent these ulcers is through frequent repositioning to redistribute body pressures. Therefore, the main goal of this study is to facilitate the timely repositioning of patients through the use of a pressure mat to identify in-bed postures in various sleep environments. Pressure data were collected from 10 healthy participants lying down on a pressure mat in 19 various in-bed postures, correlating to the supine, prone, right-side, and left-side classes. In addition, pressure data were collected from participants sitting at the edge of the bed as well as an empty bed. Each participant was asked to lie in these 19 postures in three distinct testing environments: a hospital bed, a home bed, and a home bed with a foam mattress topper. To categorize each posture into its respective class, the pre-trained 2D ResNet-18 CNN and the pre-trained Inflated 3D CNN algorithms were trained and validated using image and video pressure mapped data, respectively.</p><p><strong>Results: </strong>The ResNet-18 and Inflated 3D CNN algorithms were validated using leave-one-subject-out (LOSO) and leave-one-environment-out (LOEO) cross-validation techniques. LOSO provided an average accuracy of 92.07% ± 5.72% and 82.22% ± 8.50%, for the ResNet-18 and Inflated 3D CNN algorithms, respectively. Contrastingly, LOEO provided a reduced average accuracy of 85.37% ± 14.38% and 77.79% ± 9.76%, for the ResNet-18 and Inflated 3D CNN algorithms, respectively.</p><p><strong>Conclusion: </strong>These pilot results indicate that the proposed algorithms can accurately distinguish between in-bed postures, on unseen participant data as well as unseen mattress environment data. The proposed algorithms can establish the basis of a decubitus ulcer prevention platform that can be applied to various sleeping environments. To the best of our knowledge, the impact of mattress stiffness has not been considered in previous studies regarding in-bed posture monitoring.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"34"},"PeriodicalIF":2.9000,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10941443/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BioMedical Engineering OnLine","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1186/s12938-024-01227-x","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Decubitus ulcers are prevalent among the aging population due to a gradual decline in their overall health, such as nutrition, mental health, and mobility, resulting in injury to the skin and tissue. The most common technique to prevent these ulcers is through frequent repositioning to redistribute body pressures. Therefore, the main goal of this study is to facilitate the timely repositioning of patients through the use of a pressure mat to identify in-bed postures in various sleep environments. Pressure data were collected from 10 healthy participants lying down on a pressure mat in 19 various in-bed postures, correlating to the supine, prone, right-side, and left-side classes. In addition, pressure data were collected from participants sitting at the edge of the bed as well as an empty bed. Each participant was asked to lie in these 19 postures in three distinct testing environments: a hospital bed, a home bed, and a home bed with a foam mattress topper. To categorize each posture into its respective class, the pre-trained 2D ResNet-18 CNN and the pre-trained Inflated 3D CNN algorithms were trained and validated using image and video pressure mapped data, respectively.

Results: The ResNet-18 and Inflated 3D CNN algorithms were validated using leave-one-subject-out (LOSO) and leave-one-environment-out (LOEO) cross-validation techniques. LOSO provided an average accuracy of 92.07% ± 5.72% and 82.22% ± 8.50%, for the ResNet-18 and Inflated 3D CNN algorithms, respectively. Contrastingly, LOEO provided a reduced average accuracy of 85.37% ± 14.38% and 77.79% ± 9.76%, for the ResNet-18 and Inflated 3D CNN algorithms, respectively.

Conclusion: These pilot results indicate that the proposed algorithms can accurately distinguish between in-bed postures, on unseen participant data as well as unseen mattress environment data. The proposed algorithms can establish the basis of a decubitus ulcer prevention platform that can be applied to various sleeping environments. To the best of our knowledge, the impact of mattress stiffness has not been considered in previous studies regarding in-bed posture monitoring.

利用体压分布图进行床内体位监测,预防褥疮的发生。
背景:由于营养、心理健康和活动能力等整体健康水平逐渐下降,皮肤和组织受到损伤,褥疮在老年人群中十分普遍。预防褥疮最常用的方法是经常调整体位,重新分配身体压力。因此,本研究的主要目的是通过使用压力垫来识别各种睡眠环境中的床上姿势,从而帮助患者及时调整体位。研究人员收集了 10 名健康参与者躺在压力垫上的 19 种不同卧姿的压力数据,分别与仰卧、俯卧、右侧卧和左侧卧相关。此外,还收集了坐在床边和空床上的参与者的压力数据。每位参与者都被要求在三种不同的测试环境中以这 19 种姿势躺卧:医院病床、家用病床和带有泡沫床垫的家用病床。为了将每种姿势归入相应的类别,预先训练的二维 ResNet-18 CNN 和预先训练的充气 3D CNN 算法分别使用图像和视频压力映射数据进行了训练和验证:结果:ResNet-18 和充气三维 CNN 算法使用 "排除一个受试者"(LOSO)和 "排除一个环境"(LOEO)交叉验证技术进行了验证。LOSO 为 ResNet-18 和 Inflated 3D CNN 算法提供的平均准确率分别为 92.07% ± 5.72% 和 82.22% ± 8.50%。相反,对于 ResNet-18 和膨胀三维 CNN 算法,LOEO 的平均准确率分别为 85.37% ± 14.38% 和 77.79% ± 9.76%:这些试验结果表明,所提出的算法可以在未见过的参与者数据和未见过的床垫环境数据上准确区分床上姿势。所提出的算法可为褥疮预防平台奠定基础,该平台可应用于各种睡眠环境。据我们所知,在以往有关床上姿势监测的研究中,还没有考虑过床垫硬度的影响。
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
BioMedical Engineering OnLine
BioMedical Engineering OnLine 工程技术-工程:生物医学
CiteScore
6.70
自引率
2.60%
发文量
79
审稿时长
1 months
期刊介绍: BioMedical Engineering OnLine is an open access, peer-reviewed journal that is dedicated to publishing research in all areas of biomedical engineering. BioMedical Engineering OnLine is aimed at readers and authors throughout the world, with an interest in using tools of the physical and data sciences and techniques in engineering to understand and solve problems in the biological and medical sciences. Topical areas include, but are not limited to: Bioinformatics- Bioinstrumentation- Biomechanics- Biomedical Devices & Instrumentation- Biomedical Signal Processing- Healthcare Information Systems- Human Dynamics- Neural Engineering- Rehabilitation Engineering- Biomaterials- Biomedical Imaging & Image Processing- BioMEMS and On-Chip Devices- Bio-Micro/Nano Technologies- Biomolecular Engineering- Biosensors- Cardiovascular Systems Engineering- Cellular Engineering- Clinical Engineering- Computational Biology- Drug Delivery Technologies- Modeling Methodologies- Nanomaterials and Nanotechnology in Biomedicine- Respiratory Systems Engineering- Robotics in Medicine- Systems and Synthetic Biology- Systems Biology- Telemedicine/Smartphone Applications in Medicine- Therapeutic Systems, Devices and Technologies- Tissue Engineering
×
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:481959085
Book学术官方微信