## 1 Introduction
WiMorse [9] utilizes CSI to detect finger-generated Morse code by presenting a training-free signal conversion framework that automatically maps positional changes to standardized gesture patterns, thereby enabling accurate character recognition. In Wi-SL [10], features of CSI are combined with a support vector machine enhanced by K-means clustering and the Bagging algorithm to classify twelve distinct sign language gestures. The WiReader system [11] proposes a WiFi-based adaptive framework for in-air handwriting recognition. It employs activity factors for data segmentation, applies CSI ratio-based signal transformation, generates energy feature matrices, and incorporates them into an LSTM architecture to accurately classify handwriting gestures. This study proposes an innovative recognition method to address the limitations of traditional WiFi gesture recognition techniques in complex environments. The approach incorporates a squeeze-excitation mechanism and the VGG16 network. First, gesture action CSI data are collected using commercial WiFi devices; second, the data are preprocessed, and the short-time Fourier transform is applied to extract Doppler frequency shift images as features; then, the CSI signals are enhanced using a squeeze-excitation mechanism to reinforce gesture-related features while effectively suppressing background noise; and finally, the robust feature extraction and classification capabilities of the VGG16 network facilitate gesture recognition. This research develops a novel WiFi-enabled gesture recognition framework that addresses the critical shortcomings of existing vision and sensor-based approaches in cluttered environments. By utilizing channel state information (CSI) and deep neural networks, robust gesture recognition is achieved. The proposed solution integrates a squeeze-excitation mechanism with the VGG16 network architecture, implementing the following processing pipeline:  
1. Data Acquisition: Commercial WiFi devices capture CSI data corresponding to gesture actions. 2. Preprocessing: The acquired CSI measurements are processed using STFT-based spectral analysis to derive Doppler shift signatures in the time-frequency domain. 3. Feature Enhancement: The squeeze-excitation mechanism amplifies relevant gesture features while effectively suppressing background noise in the CSI signals. 4. Classification: The enhanced features are processed by the VGG16 network, leveraging its robust feature extraction and classification capabilities for final gesture recognition. The rest of this paper is organized as follows. Section 2 reviews related work on WiFi-based gesture recognition, analyzing previous methods and their limitations. Section 3 details the proposed SE-WiGR method, covering data acquisition, preprocessing, and the integration of squeeze and excitation networks with VGG16. Section 4 presents the experimental design and results, including deployment, data collection, performance evaluations of different algorithms, robustness tests, and ablation studies. Section 5 concludes the study by summarizing contributions, discussing limitations, and suggesting future directions. To enhance the readability of technical terms, the following Table 1 lists the main acronyms used in this paper with their full names and explanations: