Toward Single Occupant Activity Recognition for Long-Term Periods via Channel State Information

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
.