DL presents distinct advantages over traditional ML approaches as it surpasses the limitations of manual feature extraction and exhibits enhanced efficiency in handling large datasets. Additionally, Graphics Processing Units (GPUs) can be employed to accelerate the computational speed of DL models. Over the past few years, the improvement of DL techniques has become increasingly prominent in CSI-based HAR [19]. Among these techniques, the LSTM method has emerged as a notable strategy that relies solely on the current state of CSI for learning. LSTM excels at autonomously learning representative features and capturing temporal data during the feature learning process. To attain superior performance in HAR utilizing CSI measurements, researchers have developed an attention-based bidirectional LSTM method. This method combines a Bidirectional LSTM (BLSTM) [20] with an attention model that assigns increased weights to specific time steps, effectively enhancing recognition efficiency. The conventional LSTM is limited to processing sequential CSI measurements in a single direction, such as forward, and it solely relies on historical CSI information for the current hidden state. However, we emphasize the significance of incorporating future CSI data to enable accurate HAR. Additionally, the sequentially learned features of a conventional LSTM can have varying effects on the HAR task. In the conventional LSTM method, each learned characteristic contributes equally to the final identification of human activities. Real-time applications can benefit from employing advanced DL approaches and models to enhance the accuracy of these methods. Therefore, this article presents CNN-GRU-AttNet, an innovative DL network specifically designed for extracting spatial-temporal features from raw WiFi CSI data. The network architecture comprises convolution layers and a gated recurrent unit (GRU) layer. Moreover, we incorporate an attention mechanism that dynamically assigns weights to important features and time steps, thereby enhancing the model's generalization performance for HAR. To evaluate the effectiveness of our proposed model, we conduct a comprehensive set of experiments and compare its performance against existing benchmark approaches. The main contributions of this research can be succinctly summarized as:  
* Development of a novel DL framework that enables HAR using WiFi CSI measurements, eliminating the need for manual feature extraction. * Introduction of a hybrid DL network, CNN-GRU-AttNet, that leverages the strengths of CNN and GRU to automatically extract spatial and temporal features, leading to highly accurate HAR results. * Integration of an attention mechanism into the CNN-GRU-AttNet network, allowing for the prioritization of important features and time steps, thereby enhancing recognition performance. * Thorough evaluation of the proposed approach through a series of rigorous experiments, demonstrating its superior performance in HAR using WiFi CSI data. The paper follows the subsequent structure: Section 2 presents an extensive review of existing research on HAR utilizing WiFi CSI data. In Section 3, we introduce the framework for automatic learning and selection of features in the HAR process, along with the detailed description of the proposed CNN-GRU-AttNet model. The experimental setup and the results obtained under various scenarios are outlined in Section 4. Section 5 provides an in-depth discussion of the experimental findings, analyzing their implications and significance. Lastly, Section 6 concludes the study by summarizing the key findings and suggesting possible avenues for planned research endeavors.