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The Impacts of CSI Temporal Variations on CSI-based Occupancy Monitoring Systems: An Exploratory Study

DC Field Value Language
dc.contributor.authorLee, Hoonyong-
dc.contributor.authorAhn, Changbum Ryan-
dc.contributor.authorChoi, Nakjung-
dc.date.accessioned2024-05-20T06:12:51Z-
dc.date.available2024-05-20T06:12:51Z-
dc.date.created2024-05-20-
dc.date.issued2020-
dc.identifier.citationBuildSys 2020 - Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, pp.278-281-
dc.identifier.urihttps://hdl.handle.net/10371/203456-
dc.description.abstractChannel State Information (CSI) has been used for an alternative sensing source for occupancy monitoring systems to classify activities of daily living (ADLs). Previous studies have proposed learning-based activity classification models, which require similar distributions of CSI for training and testing datasets. However, as CSI varies even in a static environment, the activity classification model trained with data collected in a particular day would be invalid for other time frames. In this context, this study examines the impacts of the CSI temporal variations on the learning-based occupant activity monitoring systems. An experiment was performed to collect the CSI data while an occupant performed daily activities for six days. Three learning-based activity classification models reconstructed from the previous studies were trained and tested with time-dependent cross validation. The performances of the benchmark models were greatly degraded (below 60%) with testing data collected at different days than the training data, while their performances with testing data collected at the same day with training data were over 90%. This study also explores the opportunity to address this issue with transfer learning techniques.-
dc.language영어-
dc.publisherAssociation for Computing Machinery, Inc-
dc.titleThe Impacts of CSI Temporal Variations on CSI-based Occupancy Monitoring Systems: An Exploratory Study-
dc.typeArticle-
dc.identifier.doi10.1145/3408308.3427624-
dc.citation.journaltitleBuildSys 2020 - Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation-
dc.identifier.scopusid2-s2.0-85097182644-
dc.citation.endpage281-
dc.citation.startpage278-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorAhn, Changbum Ryan-
dc.type.docTypeConference Paper-
dc.description.journalClass1-
dc.subject.keywordAuthorChannel State Information-
dc.subject.keywordAuthorIndoor Activity Classification-
dc.subject.keywordAuthorLearning-based Classification-
dc.subject.keywordAuthorTemporal Variation-
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  • College of Engineering
  • Department of Architecture & Architectural Engineering
Research Area Computing in Construction, Management in Construction

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