Human Behavior Recognition Method Based on WiFi Channel Status Information

Human sensing technology such as human behavior recognition has gained tremendous attention in recent years due to the continuous development of artificial intelligence technology. With the development of wireless communication technology, WiFi based human sensing technology has received extensive attention due to its advantages of high popularity, low cost, difficulty in revealing user privacy, and being protected from light. In this paper, we proposed a human behavior recognition model based on DWT-SVM. Firstly, the channel state information (CSI) amplitude data is analyzed by wavelet transform, and the low-frequency wavelet coefficients after discrete wavelet transform (DWT) are selected as feature vectors. The low frequency wavelet coefficients of all channel data of CSI are extracted and spliced into a feature vector. Then a support vector machine (SVM) classifier is designed to realize human behavior recognition. Finally, the performance of the algorithm is verified on the experimental platform developed in this paper, and the performance of the proposed method outperforms other algorithms.