{"title":"Opportunistic physical activity monitoring via passive WiFi radar","authors":"Wenda Li, Bo Tan, R. Piechocki, I. Craddock","doi":"10.1109/HealthCom.2016.7749458","DOIUrl":null,"url":null,"abstract":"Physical activity envelope provides invaluable information in numerous pervasive health applications. Physical activity is traditionally gleaned using a range of wearable inertial sensors and/or video technology. This paper introduces a novel opportunistic and non-intrusive monitoring system which can quantify activity levels based on analysis of ambient WiFi signal scatter. A real-time signal processing framework is developed, and the proposed system is implemented in software defined radio platform. Experimental results corroborate the efficacy of the proposed system in long term ADL monitoring in residential healthcare applications.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HealthCom.2016.7749458","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14
Abstract
Physical activity envelope provides invaluable information in numerous pervasive health applications. Physical activity is traditionally gleaned using a range of wearable inertial sensors and/or video technology. This paper introduces a novel opportunistic and non-intrusive monitoring system which can quantify activity levels based on analysis of ambient WiFi signal scatter. A real-time signal processing framework is developed, and the proposed system is implemented in software defined radio platform. Experimental results corroborate the efficacy of the proposed system in long term ADL monitoring in residential healthcare applications.