# Wi-GC: A Deep Spatiotemporal Gesture Recognition Method Based on Wi-Fi Signal
## 2 Related Work
### Unbound Gesture Recognition
Although the gesture recognition of millimeter wave radar has high precision, its signal attenuation is significant and the cost is high. The popularity and discovery of commercial Wi-Fi network infrastructure make up for the shortcomings of the above three technologies and provide solutions. Since CSI in Wi-Fi signals is fine-grained physical information, which comes from sub-carriers decoded in Orthogonal Frequency Division Multiplexing (OFDM) systems, it is susceptible to the environment. Therefore, it has received extensive attention from researchers in recent years. For example, [PERSON] et al. [25] proposed a CSI-based sign language recognition method. This method uses the wavelet function to remove the noise in the environment. It uses the K-means combined with the Bagging algorithm to optimize the SVM classification model and the average recognition rate reaches 95.8%. [PERSON] et al. [26] proposed a CSI-based aerial handwritten digit recognition system. First, the system selects the data that can reflect the gesture movement from the CSI raw data. At the same time, noise reduction processing is performed on the selected data. After processing, the amplitude and phase information features are extracted and the S-DTW algorithm matches and recognizes different air gestures. The average recognition accuracy of each action is over 93%. [PERSON] et al. [27] proposed a fine-grained gesture recognition method, denoising CSI amplitude with wavelet transform and use conjugate calibration to eliminate the phase shift of CSI. Then a Generative Adversarial Network (GAN) is used to enhance data, Deep Neural Network (DNN) to learn transferable features for domain adaptation, softmax to classify the extracted features, and gesture recognition. The average accuracy is 94.5%. [PERSON] et al. [28] proposed a gesture recognition system based on Wi-Fi transmission physical layer CSI. First, the Savitzky-Golay filter is used to smooth the phase curve and amplitude of CSI, then 1D-CNN is used to extract gesture features, and finally, SVM is used for classification. The recognition rate of gestures is about 90%. [PERSON] et al. [29] propose a gesture recognition method based on Wi-Fi signals. The amplitude and phase of the CSI are first extracted from the Wi-Fi signal, the amplitude is filtered and the phase is expanded and linearly transformed. The CSI signal is then converted to a Red Green Blue (RGB) image using normalization and interpolation. Finally, the combined amplitude and phase RGB images are classified using a lightweight deep network model based on MobileNet_V2. [PERSON] et al. [30] propose a gesture recognition system based on a matched average joint learning framework (WiMA). The system uses parameter-matching collaborative learning to train a gesture prediction model. Static bias in the data is first removed using CSI amplitude conjugate multiplication and noise and random bias are removed using a band-pass filter. The data's Doppler Frequency Shift (DFS) spectrum is extracted, and a Body-coordinate Velocity Profile (BVP) is generated. The features of the BVP are then extracted using CNN-LSTM and finally, the gesture features are classified using softmax. WiFi-Based Low-Complexity Gesture Recognition Using Categorization. [PERSON] et al. (2018) propose a low-complexity gesture classification recognition based on Wi-Fi. The CSI data is first processed using different techniques and the pre-processed data is segmented. Then features are extracted using a deep degree learning model and finally, the extracted features are classified using SVM. [PERSON] et al. (2018) proposed a gesture recognition scheme based on the multi-modal Gaussian mixture model (GMM).