CSI-based Human Behavior Segmentation and Recognition Using Commodity Wi-Fi

Abstract

In recent years, the behavior recognition technology based on Wi-Fi devices has been favored by many researchers. Existing Wi-Fi-based human behavior recognition technology mainly uses classifification algorithms to construct classifification models,which has problems such as inaccurate behavior segmentation, failure to extract deep-level features from the original data and design classifification models matching the proposed features in the process of behavior recognition. In order to solve above problems, this paper proposes a window variance comparison method by combining the adaptive thresholds calculated to achieve effffective segmentation of multiple discontinuous human behaviors; then uses the short-time Fourier algorithm to extract time-frequency features of individual behavior, and extracts the graph structure data from the autocorrelation matrix of time-frequency features and the features themselves. A graph neural network is built for behavior recognition. The experimental results show that the segmentation accuracy of the behavior segmentation method in the two scenes is 0.964 and 0.993, which is better than the existing threshold-based behavior segmentation methods. In addition, this paper extracts graph structure data by spectral energy change method and builds behavior recognition model by using graph neural network, and the recognition accuracy is signifificantly improved compared with the traditional classifification algorithm.

Texture features-based lightweight passive multi-state crowd counting algorithm

“…The raw CSI amplitude data not only contains outliers affecting crowd counting, but also contains high-frequency noise caused by the surrounding environments and multipath effects. CSI amplitude changes caused by human bodies and their activities are mainly concentrated in the low-frequency part of CSI amplitude data, so CSI-based wireless sensing algorithms generally use low-pass filters to filter CSI amplitude data [20], such as moving average filter, Gaussian filter [8], and wavelet threshold method [21]. This paper focuses on reducing the time complexity of the algorithm, so we use the moving average filter with lower time complexity [22] to filter the high-frequency noise of CSI amplitude data.…”

Human Behavior Segmentation and Recognition Using a Single-camera

“…In related research on behavior recognition, X. Yang et al [4] proposed a behavior segmentation and recognition method based on CSI, but it is not aimed at the segmentation of continuous human behavior. W. Xing et al [5] proposed a behavior segmentation method based on posture histograms and adjusted sliding windows.…”