| Title | Abnormal human behavior detection based on VAE-LSTM hybrid model in WiFi CSI with PCA |
|---|---|
| ID_Doc | 44380 |
| Authors | Kim, Y; Kim, SC |
| Title | Abnormal human behavior detection based on VAE-LSTM hybrid model in WiFi CSI with PCA |
| Year | 2023 |
| Published | |
| Abstract | Recently, It is easy to find network access points(APs), which can be used for more than simply connecting devices to the Internet. For example, the waveform of a WiFi signal changes when a human action is performed between the two APs. In previous research, we demonstrated how changes in an electric wave affect the channel state information of a signal and how deep learning can utilize this information to detect and predict human behavior. In this paper, we proposed a method to detect human behavior. The proposed method improves the performance of detection of human behavior and effective in a changing environment. We found that using a VAE-LSTM hybrid model with PCA is useful in terms of detecting abnormal human behavior Experimental results demonstrate that the proposed method can detect general abnormal behavior with >-79% overall precision in a changing environment. |
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