• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

迈向减轻压力性损伤:从垂直床反作用力检测患者体位

Toward mitigating pressure injuries: Detecting patient orientation from vertical bed reaction forces.

作者信息

Wong Gordon, Gabison Sharon, Dolatabadi Elham, Evans Gary, Kajaks Tara, Holliday Pamela, Alshaer Hisham, Fernie Geoff, Dutta Tilak

机构信息

KITE, Toronto Rehabilitation Institute - University Health Network, Toronto, Ontario, Canada.

Department of Surgery, University of Toronto, Toronto, Ontario, Canada.

出版信息

J Rehabil Assist Technol Eng. 2020 Apr 6;7:2055668320912168. doi: 10.1177/2055668320912168. eCollection 2020 Jan-Dec.

DOI:10.1177/2055668320912168
PMID:32284876
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7137131/
Abstract

INTRODUCTION

Prolonged bed rest without repositioning can lead to pressure injuries. However, it can be challenging for caregivers and patients to adhere to repositioning schedules. A device that alerts caregivers when a patient has remained in the same orientation for too long may reduce the incidence and/or severity of pressure injuries. This paper proposes a method to detect a person's orientation in bed using data from load cells placed under the legs of a hospital grade bed.

METHODS

Twenty able-bodied individuals were positioned into one of three orientations (supine, left side-lying, or right side-lying) either with no support, a pillow, or a wedge, and the head of the bed either raised or lowered. Breathing pattern characteristics extracted from force data were used to train two machine learning classification systems (Logistic Regression and Feed Forward Neural Network) and then evaluate for their ability to identify each participant's orientation using a leave-one-participant-out cross-validation.

RESULTS

The Feed Forward Neural Network yielded the highest orientation prediction accuracy at 94.2%.

CONCLUSIONS

The high accuracy of this non-invasive system's ability to a participant's position in bed shows potential for this algorithm to be useful in developing a pressure injury prevention tool.

摘要

引言

长时间卧床且不重新调整体位会导致压疮。然而,护理人员和患者要坚持重新调整体位的时间表可能具有挑战性。一种在患者保持同一姿势时间过长时向护理人员发出警报的设备,可能会降低压疮的发生率和/或严重程度。本文提出了一种利用放置在医院病床床腿下方的称重传感器数据来检测患者在床上体位的方法。

方法

20名身体健全的个体被放置在三种体位(仰卧、左侧卧或右侧卧)之一,分别有无支撑物、一个枕头或一个楔形物,并且床头要么抬高要么降低。从力数据中提取的呼吸模式特征被用于训练两个机器学习分类系统(逻辑回归和前馈神经网络),然后使用留一法交叉验证评估它们识别每个参与者体位的能力。

结果

前馈神经网络的体位预测准确率最高,为94.2%。

结论

这种非侵入性系统检测参与者在床上体位的能力具有很高的准确率,表明该算法在开发预防压疮工具方面具有潜在用途。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b8a4/7137131/ef85a020085a/10.1177_2055668320912168-fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b8a4/7137131/73be4b9b4ea2/10.1177_2055668320912168-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b8a4/7137131/1e2cb32a74c6/10.1177_2055668320912168-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b8a4/7137131/421e0c0cbe29/10.1177_2055668320912168-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b8a4/7137131/37bf956cae74/10.1177_2055668320912168-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b8a4/7137131/90a0507bc34e/10.1177_2055668320912168-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b8a4/7137131/ef85a020085a/10.1177_2055668320912168-fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b8a4/7137131/73be4b9b4ea2/10.1177_2055668320912168-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b8a4/7137131/1e2cb32a74c6/10.1177_2055668320912168-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b8a4/7137131/421e0c0cbe29/10.1177_2055668320912168-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b8a4/7137131/37bf956cae74/10.1177_2055668320912168-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b8a4/7137131/90a0507bc34e/10.1177_2055668320912168-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b8a4/7137131/ef85a020085a/10.1177_2055668320912168-fig6.jpg

相似文献

1
Toward mitigating pressure injuries: Detecting patient orientation from vertical bed reaction forces.迈向减轻压力性损伤:从垂直床反作用力检测患者体位
J Rehabil Assist Technol Eng. 2020 Apr 6;7:2055668320912168. doi: 10.1177/2055668320912168. eCollection 2020 Jan-Dec.
2
Detecting Patient Position Using Bed-Reaction Forces for Pressure Injury Prevention and Management.利用床对压力反应力检测患者体位,以预防和管理压疮。
Sensors (Basel). 2024 Oct 9;24(19):6483. doi: 10.3390/s24196483.
3
Measuring Repositioning in Home Care for Pressure Injury Prevention and Management.测量家庭护理中压疮预防和管理的重新定位。
Sensors (Basel). 2022 Sep 16;22(18):7013. doi: 10.3390/s22187013.
4
5
A novel in-bed body posture monitoring for decubitus ulcer prevention using body pressure distribution mapping.一种利用体压分布图谱进行卧床体位监测以预防压疮的新方法。
Biomed Eng Online. 2024 Mar 15;23(1):34. doi: 10.1186/s12938-024-01227-x.
6
Detection and classification methodology for movements in the bed that supports continuous pressure injury risk assessment and repositioning compliance.用于支持持续压力性损伤风险评估和重新定位依从性的床上运动检测与分类方法。
J Tissue Viability. 2019 Feb;28(1):7-13. doi: 10.1016/j.jtv.2018.12.001. Epub 2018 Dec 24.
7
Prevention of Pressure Ulcers Among People With Spinal Cord Injury: A Systematic Review.脊髓损伤患者压疮的预防:一项系统评价
PM R. 2015 Jun;7(6):613-36. doi: 10.1016/j.pmrj.2014.11.014. Epub 2014 Dec 18.
8
Artificial Neural Network for in-Bed Posture Classification Using Bed-Sheet Pressure Sensors.基于床褥压力传感器的卧床姿态分类人工神经网络
IEEE J Biomed Health Inform. 2020 Jan;24(1):101-110. doi: 10.1109/JBHI.2019.2899070. Epub 2019 Feb 13.
9
Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.在流行地区,服用抗叶酸抗疟药物的人群中,叶酸补充剂与疟疾易感性和严重程度的关系。
Cochrane Database Syst Rev. 2022 Feb 1;2(2022):CD014217. doi: 10.1002/14651858.CD014217.
10
Development and clinical application of a computer-aided real-time feedback system for detecting in-bed physical activities.用于检测床上身体活动的计算机辅助实时反馈系统的开发与临床应用。
Comput Methods Programs Biomed. 2017 Aug;147:11-17. doi: 10.1016/j.cmpb.2017.05.014. Epub 2017 Jun 12.

引用本文的文献

1
Detecting Patient Position Using Bed-Reaction Forces for Pressure Injury Prevention and Management.利用床对压力反应力检测患者体位,以预防和管理压疮。
Sensors (Basel). 2024 Oct 9;24(19):6483. doi: 10.3390/s24196483.
2
Monitoring muscle activity in pediatric SCI: Insights from sensorized rocking chairs and machine-learning.监测小儿脊髓损伤中的肌肉活动:来自传感摇椅和机器学习的见解
J Rehabil Assist Technol Eng. 2024 Aug 28;11:20556683241278306. doi: 10.1177/20556683241278306. eCollection 2024 Jan-Dec.
3
Wearable Prophylaxis Tool for AI-Driven Identification of Early Warning Patterns of Pressure Ulcers.

本文引用的文献

1
Bed-Embedded Heart and Respiration Rates Detection by Longitudinal Ballistocardiography and Pattern Recognition.纵向振动心冲击图和模式识别检测卧床时的心率和呼吸率。
Sensors (Basel). 2019 Mar 25;19(6):1451. doi: 10.3390/s19061451.
2
Detection and classification methodology for movements in the bed that supports continuous pressure injury risk assessment and repositioning compliance.用于支持持续压力性损伤风险评估和重新定位依从性的床上运动检测与分类方法。
J Tissue Viability. 2019 Feb;28(1):7-13. doi: 10.1016/j.jtv.2018.12.001. Epub 2018 Dec 24.
3
A comparison of logistic regression models with alternative machine learning methods to predict the risk of in-hospital mortality in emergency medical admissions via external validation.
用于人工智能驱动识别压疮早期预警模式的可穿戴预防工具。
Bioengineering (Basel). 2023 Sep 25;10(10):1125. doi: 10.3390/bioengineering10101125.
4
Measuring Repositioning in Home Care for Pressure Injury Prevention and Management.测量家庭护理中压疮预防和管理的重新定位。
Sensors (Basel). 2022 Sep 16;22(18):7013. doi: 10.3390/s22187013.
通过外部验证比较逻辑回归模型与替代机器学习方法,以预测急诊入院患者住院内死亡风险。
Health Informatics J. 2020 Mar;26(1):34-44. doi: 10.1177/1460458218813600. Epub 2018 Nov 29.
4
Support surfaces for treating pressure ulcers.用于治疗压疮的支撑面。
Cochrane Database Syst Rev. 2018 Oct 11;10(10):CD009490. doi: 10.1002/14651858.CD009490.pub2.
5
Pilot study: Assessing the effect of continual position monitoring technology on compliance with patient turning protocols.试点研究:评估持续体位监测技术对患者翻身方案依从性的影响。
Nurs Open. 2017 Oct 26;5(1):21-28. doi: 10.1002/nop2.105. eCollection 2018 Jan.
6
Pressure mapping to prevent pressure ulcers in a hospital setting: A pragmatic randomised controlled trial.医院环境中预防压疮的压力映射:一项实用随机对照试验。
Int J Nurs Stud. 2017 Jul;72:53-59. doi: 10.1016/j.ijnurstu.2017.04.007. Epub 2017 Apr 21.
7
Patient Posture Monitoring System Based on Flexible Sensors.基于柔性传感器的患者体位监测系统。
Sensors (Basel). 2017 Mar 13;17(3):584. doi: 10.3390/s17030584.
8
A novel system to tackle hospital acquired pressure ulcers.一种应对医院获得性压疮的新型系统。
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:4780-4783. doi: 10.1109/EMBC.2016.7591796.
9
Revised National Pressure Ulcer Advisory Panel Pressure Injury Staging System: Revised Pressure Injury Staging System.修订后的国家压疮咨询委员会压力性损伤分期系统:修订后的压力性损伤分期系统。
J Wound Ostomy Continence Nurs. 2016 Nov/Dec;43(6):585-597. doi: 10.1097/WON.0000000000000281.
10
Pressure Relief, Visco-Elastic Foam with Inflated Air? A Pilot Study in a Dutch Nursing Home.带充气空气的减压粘弹性泡沫?荷兰一家养老院的一项试点研究。
Healthcare (Basel). 2015 Feb 12;3(1):78-83. doi: 10.3390/healthcare3010078.