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Toward Single Occupant Activity Recognition for Long-Term Periods via Channel State Information
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- Authors
- Issue Date
- 2024-01
- Citation
- IEEE Internet of Things Journal, Vol.11 No.2, pp.2796-2807
- 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.
- ISSN
- 2327-4662
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Related Researcher
- College of Engineering
- Department of Architecture & Architectural Engineering
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