W. Young, J. Corbett, M. Gerber, S. Patek, Lu Feng
{"title":"DAMON: A Data Authenticity Monitoring System for Diabetes Management","authors":"W. Young, J. Corbett, M. Gerber, S. Patek, Lu Feng","doi":"10.1109/IoTDI.2018.00013","DOIUrl":null,"url":null,"abstract":"We present DAMON, a data authenticity monitoring system for use in an Internet of Medical Things (IoMT) system assembled to treat Type 1 Diabetes (T1D). We describe the use of Signal Temporal Logic (STL) for specifying and monitoring a range of system properties relevant to T1D treatment, including constraints on glycemic variability and insulin delivery. We perform retrospective analysis of posterior probabilities of multiple meal hypotheses to detect suspicious meal events. Using a corpus of clinical study data, we provide experimental results demonstrating the detection of system events indicative of compromised data authenticity.","PeriodicalId":149725,"journal":{"name":"2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI)","volume":"225 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IoTDI.2018.00013","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
We present DAMON, a data authenticity monitoring system for use in an Internet of Medical Things (IoMT) system assembled to treat Type 1 Diabetes (T1D). We describe the use of Signal Temporal Logic (STL) for specifying and monitoring a range of system properties relevant to T1D treatment, including constraints on glycemic variability and insulin delivery. We perform retrospective analysis of posterior probabilities of multiple meal hypotheses to detect suspicious meal events. Using a corpus of clinical study data, we provide experimental results demonstrating the detection of system events indicative of compromised data authenticity.
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