## 1 Introduction
Compared with the above methods, several studies [22; 23; 24; 25] have shown that WiFi-based human action recognition has been favored by many researchers in recent years due to many advantages such as wide coverage, strong privacy protection, low price, the ability to detect non-visual distance, and no need to wear or touch. The WiFi-based human action recognition mainly relies on two signals: (1) the received signal strength indicator (RSSI); and (2) channel state information (CSI). The RSSI signal strength is more influenced by interference and multipath effects. In contrast, CSI is a fine-grained signal and has been shown to outperform RSSI signals in complex environments for applications such as micro-motion detection [26], pose estimation [27], and handwriting recognition [28]. [PERSON] et al. [29] used a bidirectional long- and short-term memory attention mechanism (ABLSTM) to train an action model for human action recognition. [PERSON] et al. [30] proposed a dynamic antenna selection algorithm based on maximum distance to achieve human action classification using CSI signals. [PERSON] et al. [31] proposed a multi-environment human activity recognition system based on CSI signals. Using an SVM classifier, they achieved an accuracy of 91.27%. [PERSON] et al. [32] converted CSI data into images and then used these images as input to a 2D convolutional neural network (CNN) classifier. Experiments showed that seven actions achieved approximately 95% accuracy. [PERSON] et al. [33] used an adaptive activity segmentation algorithm for classification using wavelet CNN, which performed well on a real dataset. The above methods using CSI data for human activity recognition still have limitations in feature extraction and recognition accuracy, and there is still room for improvement. From the above discussion, we can clearly understand that there are various methods to recognize human behavior based on CSI signals, including feature extraction, machine learning, and deep learning. However, CSI data contains both temporal and spatial information, and a model with robustness is needed to learn the features of both aspects. In this paper, we propose the SM-TCNNET model, which can effectively use both kinds of information and has high performance for human activity recognition. The main work of this paper includes:  
* A deep-learning-based model (SM-TCNNET) is proposed. The SM module is mainly used to extract the spatial features in CSI signals. The TCN probes the temporal information in the CSI signals, which improves the expressiveness of the model and its ability to accurately identify human activities. * The model requires only a small amount of pre-processing of the CSI data, which makes it easily scalable to other activity recognition datasets. * It verifies the accuracy of the SM-TCNNET model at various distances between the transmitter and the receiver. When the distance between the transmitter and the receiver is known, the distance from the human body to the connection line between the two is different. It evaluates the reliability and validity of CSI data, and it investigates the law of signal characteristics changing with distance. * The performance of SM-TCNNET is evaluated using both the self-harvested dataset and the public dataset under various scenarios of line-of-sight (LOS) and non-line-of-sight (NLOS). In the self-harvested dataset, the accuracy of human activity recognition reaches 99.93% and 97.56% in different situations of LOS and NLOS, respectively; in the public dataset, our model detects human activity with 99.8% accuracy. The main structure of this work Is organized as follows: Section 2 presents a detailed description of the used dataset. Section 3 presents the system design, including data pre-processing, model introduction, training, and testing presentation. Section 4 presents the experimental results of SM-TCNNET. Section 5 concludes the paper.