sensors, such as accelerometers, gyroscopes, magnetometers, and acoustic sensors, to extract high-level information about human behavior. However, S-HAR has limitations in terms of environmental requirements, and people may object to using sensors due to their bothersome or cumbersome nature. V-HAR and S-HAR have challenges to overcome, but they can potentially provide valuable insights into human behavior. Despite the numerous methods developed in recent years, WiFi-based sensing techniques have gained significant attention due to their widespread availability, versatility, and high performance [10]. WiFi-based sensing has the potential to integrate sensing and communication functions, as channel information can be utilized for both purposes [11]. Compared to V-HAR and S-HAR techniques, WiFi-based HAR systems provide several advantages. WiFi-based HAR systems differ from V-HAR systems in that they are not influenced by lighting conditions or variations in human body shapes, and they also respect user privacy. Additionally, these systems provide a more convenient option for smart home and healthcare applications since they do not rely on users wearing sensors. Consequently, researchers have actively engaged in investigating and developing WiFi-based HAR methods in recent times. Wi-Fi-based human HAR systems offer a cost-effective and seamless integration solution within existing Wi-Fi infrastructures in both residential and commercial environments, with minimal additional expenses. These systems can be arranged into two main types [12] based on their utilization of the received signal strength indicator (RSSI) [13], while the other type relies on channel state information (CSI) [10; 14] for activity recognition tasks. The CSI provides a comprehensive characterization of the radio frequency (RF) signal propagation, encompassing aspects such as amplitude attenuation, time lag, and phase shift across various carrier frequencies. Prior research has consistently demonstrated the superior performance of CSI-based HAR systems compared to RSSI-based alternatives [14], primarily due to the increased richness and informational content provided by CSI data. Learning-based approaches have emerged as potent tools for classification and prediction, occupying a crucial role in HAR and the implementation of recognition models. Researchers have extensively employed conventional machine learning (ML) techniques, including Hidden Markov Model [15], Random Forest [16], Support Vector Machine [17], and K-Nearest Neighbor [18], to achieve HAR objectives. In conventional activity recognition methods, ML algorithms manually extract features from sensor data, often relying on statistical or structural attributes such as means, medians, and standard deviations. Extracting the most relevant manual features often demands domain expertise. While these hand-crafted features demonstrate satisfactory performance in scenarios with limited training data, their extraction becomes increasingly intricate as the number of sensors escalates. Deep learning (DL), a cutting-edge approach within the realm of ML, has gained significant traction due to its remarkable capability to extract features and perform classification simultaneously. In contrast to traditional ML methods, DL leverages artificial neural networks with multiple layers to process data and address intricate problems. Promising outcomes have been observed across various domains through DL models including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Recurrent Neural Networks (RNN). CNN effectively overcomes high dimensionality by employing filters capable of convolving and sharing weights. On the other hand, RNN, a type of neural network, leverages previous outputs as inputs in the present step and incorporates hidden states, making it particularly suitable for solving sequential concerns and operating time series data.