## 1 Introduction  
With the advancement of Internet of Things (IoT) technology, smart homes, virtual reality, innovative healthcare, intelligent transportation, and other cutting-edge technologies have become indispensable. Researchers continue exploring more intuitive interaction methods to improve user experience and reduce the learning curve. Among these, gesture recognition--the most natural form of interaction--has emerged as a key focus in Human-Computer Interaction (HCI) research [1]. By recognizing different gestures, devices can be operated remotely or interactively. Additionally, personalized recommendations can be generated by analyzing users' movement characteristics through gesture recognition. However, low recognition accuracy may lead to operational errors, while poor real-time performance can negatively impact user experience. In recent years, gesture recognition systems have primarily utilized computer vision technology [2], infrared sensors [3], wearable devices [4], and millimeter-wave radar [5] to detect human movements. Computer vision-based approaches employ standard cameras to capture gesture videos, then extract gesture images through frame-splitting techniques for high-precision recognition. However, their effectiveness is limited by lighting conditions. In contrast, infrared technology works by sensing infrared light reflected off hand movements to create images. While it remains effective under varying lighting conditions, the potentially recorded private details in captured images raise data protection issues. This significantly restricts its deployment in scenarios requiring stringent confidentiality. Wearable-device solutions use sensors to collect pressure and acceleration data for action recognition. Although these systems perform well, they face practical challenges such as user discomfort from continuous wear and limited battery life. Millimeter-wave radar, grounded in the principles of Frequency Modulated Continuous Wave (FMCW) technology, achieves high recognition accuracy through detailed analysis of echo signal characteristics. Nevertheless, its widespread adoption is hindered by high hardware costs. The emergence of WiFi technology has offered a promising solution to overcome the limitations of the aforementioned approaches. The operation of WiFi-based detection depends mainly on two indicators: Received Signal Strength Indicator (RSSI) and Channel State Information (CSI). Historically, the RSSI has been utilized to assess the strength and quality of wireless connections by quantifying the power level of incoming signals. In WiFi sensing applications, RSSI enables device presence detection and localization. By comparing RSSI values from multiple devices, the system can estimate their relative distances from the access point (AP). In compliance with the IEEE 802.11n standard [6], Orthogonal Frequency Division Multiplexing (OFDM) is employed to modulate WiFi signals into multiple subcarriers, thereby enabling the extraction of CSI [7]. Unlike the RSSI, CSI provides a more fine-grained characterization of wireless channels, encapsulating detailed propagation characteristics such as amplitude variations, phase shifts, and frequency response characteristics. This enables precise monitoring of signal variations during transmission, yielding more accurate wireless environment sensing and analysis. CSI demonstrates exceptional sensitivity to environmental changes. Beyond detecting major movements like walking or running, it can capture subtle motions, including breathing and chewing, in static environments. Several studies have leveraged these properties for advanced gesture recognition: WiSee [8] utilizes two living room access points to extract comprehensive gesture information from wireless signals, enabling recognition of nine home-based gestures.