Measurement of Body Weight, Heart Rate, and Respiration Rate Using Microbend Fiber Sensor Integrated Air-Mattress System

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Guiyou Lin;Chuanzheng Jia;Lingna Wang;Zhihao Chen;Huicheng Yang;Xueliang Lin;Tim Liu;Weijuan Chen;Shiqin Ni
{"title":"Measurement of Body Weight, Heart Rate, and Respiration Rate Using Microbend Fiber Sensor Integrated Air-Mattress System","authors":"Guiyou Lin;Chuanzheng Jia;Lingna Wang;Zhihao Chen;Huicheng Yang;Xueliang Lin;Tim Liu;Weijuan Chen;Shiqin Ni","doi":"10.1109/JSEN.2025.3592200","DOIUrl":null,"url":null,"abstract":"A bedridden patient is someone unable to leave their bed due to physical, medical, or psychological conditions, presenting significant challenges for body weight (BW) monitoring. Conventional methods, such as specialized weighing scales or integrated hospital bed units, are commonly used but are space-intensive, costly, and operationally complex. Emerging technologies, such as pressure sensors, show potential but remain underdeveloped and not widely accessible. Moreover, existing pressure sensors are not designed for simultaneous measurement of BW, heart rate (HR), and respiratory rate (RR). To address these limitations, we propose a novel microbend fiber pressure sensor integrated into an air-mattress system, capable of simultaneously measuring BW, HR, and RR without requiring patient movement. The system leverages air pressure changes within the mattress to detect these parameters. Experimental validation revealed that RR measurements closely matched manual scoring, with a low mean absolute error (MAE) of <inline-formula> <tex-math>$1.8~\\pm ~0.4$ </tex-math></inline-formula> bpm. HR measurements demonstrated an MAE of <inline-formula> <tex-math>$1.4~\\pm ~0.2$ </tex-math></inline-formula> bpm. BW measurements exhibited the MAE of less than 1.0% <inline-formula> <tex-math>$\\pm ~0.3$ </tex-math></inline-formula>% compared to electronic scales. This innovative solution is particularly well-suited for patients unable to stand or those requiring continuous monitoring of dynamic physiological data, offering a cost-effective, compact, and efficient alternative to conventional methods.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"34583-34595"},"PeriodicalIF":4.3000,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Journal","FirstCategoryId":"103","ListUrlMain":"https://ieeexplore.ieee.org/document/11104944/","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

Abstract

A bedridden patient is someone unable to leave their bed due to physical, medical, or psychological conditions, presenting significant challenges for body weight (BW) monitoring. Conventional methods, such as specialized weighing scales or integrated hospital bed units, are commonly used but are space-intensive, costly, and operationally complex. Emerging technologies, such as pressure sensors, show potential but remain underdeveloped and not widely accessible. Moreover, existing pressure sensors are not designed for simultaneous measurement of BW, heart rate (HR), and respiratory rate (RR). To address these limitations, we propose a novel microbend fiber pressure sensor integrated into an air-mattress system, capable of simultaneously measuring BW, HR, and RR without requiring patient movement. The system leverages air pressure changes within the mattress to detect these parameters. Experimental validation revealed that RR measurements closely matched manual scoring, with a low mean absolute error (MAE) of $1.8~\pm ~0.4$ bpm. HR measurements demonstrated an MAE of $1.4~\pm ~0.2$ bpm. BW measurements exhibited the MAE of less than 1.0% $\pm ~0.3$ % compared to electronic scales. This innovative solution is particularly well-suited for patients unable to stand or those requiring continuous monitoring of dynamic physiological data, offering a cost-effective, compact, and efficient alternative to conventional methods.
利用微弯纤维传感器集成气垫系统测量体重、心率和呼吸速率
卧床病人是指由于身体、医疗或心理状况而无法离开床的人,这对体重(BW)监测提出了重大挑战。常用的传统方法,如专用称重秤或综合病床单元,但占用空间大,成本高,操作复杂。新兴技术,如压力传感器,显示出潜力,但仍然不发达,不能广泛使用。此外,现有的压力传感器不能同时测量体重、心率(HR)和呼吸频率(RR)。为了解决这些限制,我们提出了一种集成在气垫系统中的新型微弯曲纤维压力传感器,能够同时测量体重、HR和RR,而无需患者移动。该系统利用床垫内的气压变化来检测这些参数。实验验证表明,RR测量与人工评分密切匹配,平均绝对误差(MAE)为1.8~ 0.4$ bpm。HR测量显示MAE为$1.4~ $ pm ~ $ 0.2$ bpm。与电子秤相比,BW测量的误差小于1.0% ~0.3美元%。这种创新的解决方案特别适合无法站立或需要持续监测动态生理数据的患者,为传统方法提供了一种经济、紧凑和高效的替代方案。
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
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
×
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