Low power IoT platform for vital signs monitoring

Simon Coulter, M. Mostes, G. Lightbody, E. Popovici, W. Fennell
{"title":"Low power IoT platform for vital signs monitoring","authors":"Simon Coulter, M. Mostes, G. Lightbody, E. Popovici, W. Fennell","doi":"10.1109/ISSC.2017.7983641","DOIUrl":null,"url":null,"abstract":"Recent years have witnessed tremendous advances in wearable technology with many applications ranging from health and fitness, sports, security, and more recently augmented reality. Classical body area networks have been reduced to small, wearable devices such as smart watches where signal acquisition is accompanied by processing or streaming to a more powerful device such as a smart phone (or “fog”), or directly to the Cloud. We present a system based around EPIC electric potential sensors which are capable of acquiring bio-electric signals, including an electrocardiogram-like signal (ECG). The paper compares a set of validation algorithms for the extraction of Heart Rate (HR) and Respiratory Rate (RR) suitable for use on EPIC sensor data acquired with the proposed system. These algorithms are evaluated in terms of precision and the estimated robustness and variance. The system is of particular relevance in the field of Augmented and Virtual Reality, in which such a miniaturised, wireless platform becomes a necessity [1].","PeriodicalId":170320,"journal":{"name":"2017 28th Irish Signals and Systems Conference (ISSC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 28th Irish Signals and Systems Conference (ISSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSC.2017.7983641","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

Abstract

Recent years have witnessed tremendous advances in wearable technology with many applications ranging from health and fitness, sports, security, and more recently augmented reality. Classical body area networks have been reduced to small, wearable devices such as smart watches where signal acquisition is accompanied by processing or streaming to a more powerful device such as a smart phone (or “fog”), or directly to the Cloud. We present a system based around EPIC electric potential sensors which are capable of acquiring bio-electric signals, including an electrocardiogram-like signal (ECG). The paper compares a set of validation algorithms for the extraction of Heart Rate (HR) and Respiratory Rate (RR) suitable for use on EPIC sensor data acquired with the proposed system. These algorithms are evaluated in terms of precision and the estimated robustness and variance. The system is of particular relevance in the field of Augmented and Virtual Reality, in which such a miniaturised, wireless platform becomes a necessity [1].
用于生命体征监测的低功耗物联网平台
近年来,可穿戴技术取得了巨大的进步,在健康、健身、体育、安全以及最近的增强现实等领域都有很多应用。经典的身体区域网络已经缩小到小型的可穿戴设备,如智能手表,其中信号采集伴随着处理或流传输到更强大的设备,如智能手机(或“雾”),或直接到云。我们提出了一个基于EPIC电位传感器的系统,该系统能够获取生物电信号,包括类似心电图的信号(ECG)。本文比较了一组适用于EPIC传感器数据提取的心率(HR)和呼吸速率(RR)的验证算法。这些算法在精度和估计的稳健性和方差方面进行了评估。该系统在增强现实和虚拟现实领域具有特别的相关性,在该领域,这样一个小型化的无线平台变得必不可少[1]。
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:481959085
Book学术官方微信