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Toward Single Occupant Activity Recognition for Long-Term Periods via Channel State Information
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Hoonyong | - |
dc.contributor.author | Ahn, Changbum Ryan | - |
dc.contributor.author | Choi, Nakjung | - |
dc.date.accessioned | 2024-05-16T01:19:02Z | - |
dc.date.available | 2024-05-16T01:19:02Z | - |
dc.date.created | 2024-02-01 | - |
dc.date.created | 2024-02-01 | - |
dc.date.issued | 2024-01 | - |
dc.identifier.citation | IEEE Internet of Things Journal, Vol.11 No.2, pp.2796-2807 | - |
dc.identifier.issn | 2327-4662 | - |
dc.identifier.uri | https://hdl.handle.net/10371/202383 | - |
dc.description.abstract | With the rapid deployment of indoor Wi-Fi networks, channel state information (CSI) has been used for device-free occupant activity recognition (OAR). However, various environmental factors interfere with the stable propagation of Wi-Fi signals indoors, which causes temporal variation of CSI data. In this study, we investigated temporal CSI variation in a real-world housing environment and its impact on learning-based OAR. The CSI variation over time changes distributions of the CSI data, and the pretrained model's accuracy performance becomes degraded during long-term monitoring. In order to address the temporal dependency issue, we developed an effective long-term OAR model based on the semi-supervised meta-learning approach. Our model leveraged unlabeled target data with its pseudo labels and synthesized numerous query data sets using mixup-based data augmentation, which generalized the model during training. The model provided an average of 91.09% activity classification accuracy for the target data, which had different statistical characteristics from the source data. This result demonstrates that our model can reliably monitor occupant activities for long-term periods. The data set presented in this study is available in IEEE DataPort at https://dx.doi.org/10.21227/z10g-vt48. | - |
dc.language | 영어 | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Toward Single Occupant Activity Recognition for Long-Term Periods via Channel State Information | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/JIOT.2023.3296472 | - |
dc.citation.journaltitle | IEEE Internet of Things Journal | - |
dc.identifier.wosid | 001153911600021 | - |
dc.identifier.scopusid | 2-s2.0-85165295123 | - |
dc.citation.endpage | 2807 | - |
dc.citation.number | 2 | - |
dc.citation.startpage | 2796 | - |
dc.citation.volume | 11 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Ahn, Changbum Ryan | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.subject.keywordAuthor | Channel state information (CSI) | - |
dc.subject.keywordAuthor | domain shift | - |
dc.subject.keywordAuthor | meta-learning | - |
dc.subject.keywordAuthor | occupant activity recognition (OAR) | - |
dc.subject.keywordAuthor | temporal variation | - |
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- College of Engineering
- Department of Architecture & Architectural Engineering
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