## 2 Related Works
### DL for HAR  
DL techniques have gained significant traction in utilizing WiFi CSI for the purposes of localizing and classifying human actions, leveraging the wealth of wireless link information it offers. [PERSON] et al. [26] presented an indoor localization of the HAR system based on a multitasking 1D-CNN architecture enhanced with residual connections. Their model achieved a notable accuracy of 95.68% when tested on a dataset comprising six distinct categories of human behavior. [PERSON] et al. [27] gathered CSI data from various human activities and converted them into RGB images, which were then passed through a 2D-CNN layer for classification. Their best-performing model obtained an accuracy of 95%. [PERSON] et al. [28] presented a MIMO-AE for physical activity classification, which achieved a high accuracy of 94.49% using only 50% of the training data.  
In HAR, RNNs and their subsets, such as LSTM, have been commonly used for CSI data analysis. However, when analyzing long sequences, these networks face problems, leading to vanishing gradients. Even with the inclusion of long memory and switch gates in LSTM, the problem persists [20]. The memory bandwidth requirements of LSTMs are substantial by reason of the complexity of their sequential direction and MLP layers. Furthermore, these models encounter difficulties when confronted with sequences comprising a large number of terms, as their performance becomes compromised beyond 100 terms [29]. Additionally, LSTMs are restricted to analyzing sequential data in a single direction, limiting their ability to capture bidirectional dependencies. Therefore, they cannot differentiate between activities such as lying and sitting down.  
To overcome these limitations, researchers have developed new methods for HAR. [PERSON] et al. [19] created the StanWiFi dataset, extracted statistical features, and used hidden Markov models, LSTM, and RF models to classify activities with reported accuracies of 64.6%, 73.3%, and 90.5%, respectively. The BiLSTM architecture was meticulously designed to leverage both historical and prospective CSI data [30], facilitating effective feature learning in the realm of classification. Additionally, the ABLSTM algorithm [20] underwent rigorous evaluation and comparative analysis against alternative algorithms. [PERSON] et al. [31] introduced the Dense-LSTM method, which demonstrated a remarkable accuracy of approximately 90% while employing a reasonable amount of CSI data. [PERSON] et al. [32] proposed a DL model that combined LSTM-CNN with WiFi CSI signals, yielding an average performance of 94.14% on a publicly available dataset. Moreover, [PERSON] et al. [33] presented CSITime, an adjusted InceptionTime structure customized for HAR tasks using WiFi CSI signals, achieving an impressive accuracy of 98% on the StanWiFi dataset.  
As discussed earlier, several researchers have explored different techniques for HAR, including feature extraction, CNN, and RNN-based models, as shown in Table 1. The presence of spatial and temporal characteristics within WiFi CSI-based HAR data necessitates the utilization of a model capable of effectively capturing both aspects of human behavior. To address this, our study introduces a hybrid DL model that is proficient in learning spatial-temporal features, all while maintaining a streamlined parameter count. This model offers enhanced precision and accuracy for HAR tasks.