Long and Short Term Network Unet Personnel Activity Identification Based on CSI

In activity recognition, Wi-fi-based Channel State Information (CSI) has the advantage of capturing fine-grained information. In the process of action judgment, it is necessary to identify the interval in which each action occurs. For the detection of the motion interval, many methods in the past set statistical thresholds according to experience, find out special areas through sliding Windows, or determine the dynamic changes of signals at the start and end of actions through dynamic time warping. However, relying solely on empirical thresholds can be challenging due to noise interference. To address these challenges, researchers propose a novel approach using a one-dimensional convolutional neural network (CNN) to traverse the data, achieving results similar to sliding windows. This allows the CNN to consider all available data for improved performance. However, CSI data are often the distortion changes of each channel in Wi-Fi over a period of time. If the convolutional neural network is simply used, the time characteristics of the data are ignored. In this paper, conv-LSTM is used to extract the time characteristics of each layer of the data extraction network, and an overall fusion of each feature is carried out in the data fusion network. To improve the processing effect of CSI data. Besides, directly classifying actions using a convolutional network may yield low accuracy. To overcome this limitation, in this paper, we combine the bidirectional long short-term memory network to scan and classify the action interval, and improve the overall classification accuracy