{"title":"Analysis and Research on Performance of Sleep Safety Monitoring System Based on WiFi Sensing","authors":"Yi Wang, Haoran Yang, Jun Wang","doi":"10.1109/ECICE55674.2022.10042921","DOIUrl":null,"url":null,"abstract":"A series of sleep disorders such as insomnia and sleep apnea is common among the elderly. Currently, machine vision perception and contact perception monitoring are widely used. This study proposed a new system that is applied to home vital signs monitoring with commercial WiFi. The system uses commercial WiFi to perceive and identify sleep time segments through motion detection algorithms. The segments are divided into large and small-amplitude random motion states and filtered out into small-amplitude segments. Then, the CSI ratio model is used for instantaneous respiratory rate estimation. The CSI ratio model has an accuracy of over 97% and provides excellent real-time sleep safety monitoring, outperforming other popular solutions such as the maximum ratio combining technology.","PeriodicalId":282635,"journal":{"name":"2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ECICE55674.2022.10042921","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
A series of sleep disorders such as insomnia and sleep apnea is common among the elderly. Currently, machine vision perception and contact perception monitoring are widely used. This study proposed a new system that is applied to home vital signs monitoring with commercial WiFi. The system uses commercial WiFi to perceive and identify sleep time segments through motion detection algorithms. The segments are divided into large and small-amplitude random motion states and filtered out into small-amplitude segments. Then, the CSI ratio model is used for instantaneous respiratory rate estimation. The CSI ratio model has an accuracy of over 97% and provides excellent real-time sleep safety monitoring, outperforming other popular solutions such as the maximum ratio combining technology.
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