## 2 Related Work  
The ubiquitous deployment of WiFi infrastructure in indoor environments has accelerated advancements in WiFi-based gesture recognition technology. Among available signal metrics, RSS measures the power level of received signals. Several studies have demonstrated RSS-based gesture recognition systems: [PERSON] et al. [PERSON] et al. (2012) developed WiGest, an RSS--based system that classifies movements by establishing separate gesture categories. [PERSON] et al. [PERSON] et al. (2013) designed an activity recognition framework leveraging WiFi RSS measurements. This framework can identify directional gestures (Left/Right, Up/Down) and symbolic motions (infinity signs, open/close actions). The latter framework combines classification trees with K-nearest neighbor algorithms, achieving 96% recognition accuracy even in non-line-of-sight conditions. However, despite its ease of collection and implementation, RSS exhibits inherent limitations for gesture recognition. The metric fails to capture fundamental signal variations caused by human movements and demonstrates instability even in static environments [PERSON] et al. (2014), significantly constraining its practical application. CSI serves as a critical physical-layer parameter in WiFi systems, quantitatively describing the multipath propagation properties of wireless channels. It empowers transmitters \begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviation** & **Full Title** \\ \hline HCI & Human–Computer Interaction \\ RSSI & Received Signal Strength Indicator \\ CSI & Channel State Information \\ AP & Access Point \\ SVMs & Support Vector Machines \\ CNNs & Convolutional Neural Networks \\ ABLSTM & Attention-based Bidirectional LSTM \\ GRU & Gated Recurrent Unit \\ SE & Squeeze-and-Excitation \\ NICs & Network Interface Cards \\ SENet & Squeeze-and-Excitation Network \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of explanations of terms for the core abbreviations in this study. and receivers to execute intelligent adaptive strategies during wireless data transmission. Recent research has demonstrated various CSI-based gesture recognition approaches: [PERSON] et al. [15] developed WiG, which identifies distinctive patterns in anomalous CSI sequences to represent different gestures, employing Support Vector Machines (SVMs) for classification. WiGeR [16] employs CSI diffusion patterns to achieve accurate gesture recognition, utilizing a customized segmentation and windowing algorithm grounded in wavelet analysis and short-time energy computations. WiAG [17] implements a DWT-based feature extraction pipeline coupled with KNN classification. AirFi [18] proposes a domain-generalized learning framework that distills environment-invariant CSI features through multi-domain training, eliminating the need for scenario-specific calibration. [PERSON] [19] proposed a cross-domain CSI method that extracts domain-invariant features for reliable recognition in diverse environments. The system recognizes six gesture types (push/pull, sweep, hand clap, slide, circling, and sawtooth) in known and unknown domains without additional training data from new environments. Feature extraction represents a crucial stage in gesture recognition systems. While traditional manual feature extraction methods are both time-consuming and prone to omitting important implicit patterns, recent advances in deep learning have enabled automated extraction of key Channel State Information (CSI) features. Several notable approaches demonstrate this capability: [PERSON] et al. [20] developed FreeGesture, a WiFi CSI-based system that employs Convolutional Neural Networks (CNNs) to learn and extract the features from CSI frames. [PERSON] et al. [21] introduced FingerPass, a system utilizing LSTM to process temporal gesture patterns, with the derived recognition results further employed for identity verification applications.