基于雷达的家庭夜间呼吸监测的实用研究

Yindong Hua, Zongxing Xie, Fan Ye
{"title":"基于雷达的家庭夜间呼吸监测的实用研究","authors":"Yindong Hua, Zongxing Xie, Fan Ye","doi":"10.1109/RadarConf2351548.2023.10149560","DOIUrl":null,"url":null,"abstract":"Radar-based solutions support practical and longitudinal respiration monitoring owing to their non-invasive nature. Nighttime respiration monitoring at home provides rich and high-quality data, mostly free of motion disturbances because the user is quasi-stationary during sleep, and 6–8 hours per day rather than tens of minutes, promising for longitudinal studies. However, most existing work was conducted in laboratory environments for short periods, thus the environment, user motions, and postures can differ significantly from those in real homes. To understand how to obtain quality, overnight respiration data in real homes, we conduct a thorough experimental study with 6 participants of various sleep postures over 9 nights in 4 real-home testbeds, each configured with 3–4 sensors around the bed. We first compare the performance among four typical sensor placements around the bed to understand which is the optimal location for high quality data. Then we explore methods to track range bins with high quality signals as occasional user motions change the distance thus signal qualities, and different aspects of amplitude and phase data to further improve the signal quality using metrics of the periodicity-to-noise ratio (PNR) and end-to-end (e2e) accuracy. The experiments demonstrate that the sensor placement is a vital factor, and the bedside is an optimal choice considering both accuracy and ease of deployment (2.65 bpm error at 80 percentile), also consistent among four typical sleep postures. We also observe that, a proper range bin selection method can improve the PNR by 2 dB at 75-percentile, and e2e accuracy by 0.9 bpm at 80-percentile. Both amplitude and phase data have comparable e2e accuracy, while phase is more sensitive to motions thus suitable for nighttime movement detection. Based on these discoveries, we develop a few simple practical guidelines useful for the community to achieve high quality, longitudinal home-based overnight respiration monitoring.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Study of Practical Radar-based Nighttime Respiration Monitoring at Home\",\"authors\":\"Yindong Hua, Zongxing Xie, Fan Ye\",\"doi\":\"10.1109/RadarConf2351548.2023.10149560\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Radar-based solutions support practical and longitudinal respiration monitoring owing to their non-invasive nature. Nighttime respiration monitoring at home provides rich and high-quality data, mostly free of motion disturbances because the user is quasi-stationary during sleep, and 6–8 hours per day rather than tens of minutes, promising for longitudinal studies. However, most existing work was conducted in laboratory environments for short periods, thus the environment, user motions, and postures can differ significantly from those in real homes. To understand how to obtain quality, overnight respiration data in real homes, we conduct a thorough experimental study with 6 participants of various sleep postures over 9 nights in 4 real-home testbeds, each configured with 3–4 sensors around the bed. We first compare the performance among four typical sensor placements around the bed to understand which is the optimal location for high quality data. Then we explore methods to track range bins with high quality signals as occasional user motions change the distance thus signal qualities, and different aspects of amplitude and phase data to further improve the signal quality using metrics of the periodicity-to-noise ratio (PNR) and end-to-end (e2e) accuracy. The experiments demonstrate that the sensor placement is a vital factor, and the bedside is an optimal choice considering both accuracy and ease of deployment (2.65 bpm error at 80 percentile), also consistent among four typical sleep postures. We also observe that, a proper range bin selection method can improve the PNR by 2 dB at 75-percentile, and e2e accuracy by 0.9 bpm at 80-percentile. Both amplitude and phase data have comparable e2e accuracy, while phase is more sensitive to motions thus suitable for nighttime movement detection. Based on these discoveries, we develop a few simple practical guidelines useful for the community to achieve high quality, longitudinal home-based overnight respiration monitoring.\",\"PeriodicalId\":168311,\"journal\":{\"name\":\"2023 IEEE Radar Conference (RadarConf23)\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE Radar Conference (RadarConf23)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RadarConf2351548.2023.10149560\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE Radar Conference (RadarConf23)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RadarConf2351548.2023.10149560","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

基于雷达的解决方案由于其非侵入性而支持实际和纵向呼吸监测。家庭夜间呼吸监测提供了丰富和高质量的数据,大多数没有运动干扰,因为用户在睡眠期间是准静止的,每天6-8小时而不是几十分钟,有望用于纵向研究。然而,大多数现有的研究都是在实验室环境中进行的,时间很短,因此环境、用户动作和姿势可能与真实家庭中的情况有很大不同。为了了解如何在真实家庭中获得高质量的夜间呼吸数据,我们在4个真实家庭测试床上对6名参与者进行了为期9晚的不同睡眠姿势的实验研究,每个测试床周围配置了3-4个传感器。我们首先比较了床周围四种典型传感器的性能,以了解哪种位置是获得高质量数据的最佳位置。然后,我们探索了跟踪具有高质量信号的距离箱的方法,因为偶尔的用户运动改变了距离从而改变了信号质量,以及振幅和相位数据的不同方面,从而使用周期噪声比(PNR)和端到端(e2e)精度的度量进一步提高了信号质量。实验表明,传感器的放置是一个至关重要的因素,考虑到传感器的准确性和易部署性(80百分位误差2.65 bpm),床边是一个最佳选择,并且在四种典型的睡眠姿势中也是一致的。我们还观察到,适当的距离箱选择方法可以在75百分位时提高PNR 2 dB,在80百分位时提高e2e精度0.9 bpm。振幅和相位数据都具有相当的端到端精度,而相位对运动更敏感,因此适合夜间运动检测。基于这些发现,我们制定了一些简单实用的指导方针,有助于社区实现高质量、纵向的家庭夜间呼吸监测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Study of Practical Radar-based Nighttime Respiration Monitoring at Home
Radar-based solutions support practical and longitudinal respiration monitoring owing to their non-invasive nature. Nighttime respiration monitoring at home provides rich and high-quality data, mostly free of motion disturbances because the user is quasi-stationary during sleep, and 6–8 hours per day rather than tens of minutes, promising for longitudinal studies. However, most existing work was conducted in laboratory environments for short periods, thus the environment, user motions, and postures can differ significantly from those in real homes. To understand how to obtain quality, overnight respiration data in real homes, we conduct a thorough experimental study with 6 participants of various sleep postures over 9 nights in 4 real-home testbeds, each configured with 3–4 sensors around the bed. We first compare the performance among four typical sensor placements around the bed to understand which is the optimal location for high quality data. Then we explore methods to track range bins with high quality signals as occasional user motions change the distance thus signal qualities, and different aspects of amplitude and phase data to further improve the signal quality using metrics of the periodicity-to-noise ratio (PNR) and end-to-end (e2e) accuracy. The experiments demonstrate that the sensor placement is a vital factor, and the bedside is an optimal choice considering both accuracy and ease of deployment (2.65 bpm error at 80 percentile), also consistent among four typical sleep postures. We also observe that, a proper range bin selection method can improve the PNR by 2 dB at 75-percentile, and e2e accuracy by 0.9 bpm at 80-percentile. Both amplitude and phase data have comparable e2e accuracy, while phase is more sensitive to motions thus suitable for nighttime movement detection. Based on these discoveries, we develop a few simple practical guidelines useful for the community to achieve high quality, longitudinal home-based overnight respiration monitoring.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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:604180095
Book学术官方微信