# Wi-GC: A Deep Spatiotemporal Gesture Recognition Method Based on Wi-Fi Signal
## 2 Related Work
### Unbound Gesture Recognition  
Four main types of unbound gesture recognition are currently studied: Radio Frequency Identification Devices (RFID), webcam, millimeter-wave radar, and Wi-Fi technology. RFID gesture recognition recognizes gestures through the signals received by RFID reading tags. For example, [PERSON] et al. [21] propose a real-time gesture recognition system, which collects gesture phase data reflected from passive RFID tags through a Commercial Off-The-Shelf (COTS) Impinj RFID reader. Using their own extracted algorithm to denoise and smooth the data with a Moving Average (MA) filter, they obtain static gesture features by relative position calculation and segmentally extract dynamic features. Finally, the K-Nearest Neighbours (KNN) and Dynamic Time Warping (DTW) algorithm are used to recognize gestures. The gesture accuracy rate reaches 90%. However, the RFID identification method requires gesture recognition under the condition of sight distance. Gesture recognition is based on a webcam process and recognizes the collected image data. For example, [PERSON] et al. [6] firstly use the web camera to collect the picture data of gestures, thresholding to remove image noise and smooth the image, and finally use the KNN algorithm to extract and classify features, and the accuracy rate reaches 99.9%. [PERSON] et al. [22] present a novel skeleton-based dynamic gesture recognition framework. In a Spatially Perceptive stream (SP-stream), the compact joints are adaptively selected using their designed compact joint coding method for convex packages of the hand skeleton. They then encode them as skeleton images to fully extract spatial features. In addition, they provide a Global Enhancement Module (GEM) to enhance the critical feature maps. In the temporal perception stream (TP-stream), they propose a Motion Perception Module (MPM) to strengthen the significant motion of the gesture on the X/Y/Z axes. Then the Feature Aggregation Module (FAM) is used to aggregate more time dynamics. Finally, the scores obtained from the spatial-aware and time-aware streams are averaged to get the final classification results. [PERSON] et al. [23] proposed a hybrid deep-learning framework to recognize dynamic gestures. The features of each frame in the video need to be extracted to obtain temporal and dynamic information about the gestures made. Therefore, GoogleNet is used to extract the gesture features from the video. Finally, the extracted features are transferred to a Bidirectional Gated Recurrent Unit (BiGRU) network to classify gestures. [PERSON] et al. [24] proposed a new continuous dynamic gesture recognition method. They use a gesture localization module to segment a video sequence of continuous gestures into individual gestures. Three residual 3D Convolution Neural Networks based on ResNet architectures (3D_ResNet) are used to extract the RGB, optical flow and depth of the gestures features. Meanwhile, BiLSTM is used to extract the features of the 3D positions of the critical joints of the gestures. Finally, the weights of the fully connected layers are fused for gesture classification. This method has a high recognition accuracy, but the method requires a massive amount of calculation and is easily affected by lighting conditions and obstacles. At the same time, the camera has a dead monitoring angle, which can only achieve perception within a specific range under the line of sight, and violates the user's privacy. Gesture recognition based on millimeter-wave radar uses millimeter-level signals sent and received by radar equipment and processes and recognizes them. For example, [PERSON] et al. [7] use millimeter-wave radar signals for gesture recognition and convert millimeter-wave data into a Time and Space Velocity (TSV) spectrogram. Then the gesture features are extracted by a specific feature extraction algorithm and classified with a custom classifier, achieving an accuracy of 93%.