# Wi-GC: A Deep Spatiotemporal Gesture Recognition Method Based on Wi-Fi Signal
## 1 Introduction  
Gesture recognition plays a vital role in the research field of Human-Computer Interaction (HCI) [1]. It supports many emerging Internet of Things (IoT) applications such as user recognition [2], smart home [3], healthcare [4], etc. Generally, the technologies based on gesture recognition include sensors [5], web cameras [6], and millimeter-wave radars [7]. However, they all have certain limitations. For example, the sensor will cause an additional body burden to the user and its deployment and maintenance costs are high. The camera will expose the user's privacy and dead spots in the shot. Millimeter-wave radar signal attenuation is significant and the price is high. Recently, Wi-Fi-based gesture recognition methods [8] have become a hot research topic with non-contact, easy deployment, security and low-cost advantages. However, most of the current Wi-Fi-based gesture recognition methods extract the temporal features of gestures and ignore the spatial features, which affects the accuracy of gesture recognition to different degrees. To this end, we propose a method to obtain both temporal and spatial characteristics of gestures, thus improving gesture recognition accuracy in Wi-Fi-based environments. Based on the limitations of the above gesture recognition technology, with the development of wireless communication and passive sensing technology, commercial Wi-Fi has become a research hotspot in the fields of gesture recognition [9], fall detection [10], breathing and heartbeat monitoring [11] and motion perception [12]. Previous researchers have mainly used Received Signal Strength (RSS) in Wi-Fi to sense gesture activity. For example, [PERSON] et al. used RSS generated by software radio to recognize gestures and achieved about 72% recognition accuracy [13]. Since RSS belongs to the Media Access Control (MAC)layer, it is easily affected by path attenuation, occlusion and multi-path effects and has poor stability. However, CSI in Wi-Fi can compensate for RSS's shortcomings. Because it is a channel feature extracted from the PHY layer, it is a more detailed description of the channel and can also capture the multi-path variation of the signal propagation path. Therefore, CSI can clearly describe the influence of various human behaviors on signal propagation. For example, [PERSON] et al. [14] proposed a CSI-based gesture recognition system. The CIFE technique derives Bi-spectral Features (BF) from raw CSI data to explore Higher-Order Statistics (HOS) methods. The extracted features are finally classified with a Support Vector Machine (SVM) to form a subset of informative and optimal characteristics. [PERSON] et al. [15] proposed a dynamic gesture recognition algorithm based on CSI and You Only Look Once: Version 3 (YOLOv3). The data collection adopts the CSI-based radio frequency method. Grey value images are generated using adaptive weighted fusion, Kalman filtering, threshold segmentation and data transformation on the acquired data. Finally, the YOLOv3 object detection algorithm is used to train and recognize grayscale images containing continuous dynamic gesture information, with an average recognition accuracy of 94%. Therefore, compared with RSS, CSI is more stable, so the accuracy of gesture recognition is higher. Currently, most CSI-based gesture recognition researchers extract gesture features from the amplitude or phase of CSI. For gesture recognition methods, extracting gesture features is a significant part. This method only focuses on some features of gestures and ignores others. Therefore, discovering other features of CSI data becomes a new challenge. To solve the above problems, we propose a deep spatiotemporal gesture recognition method based on Wi-Fi signal (Wi-GC). The process mainly includes three stages. The first stage is data acquisition. The second stage is preprocessing: first, we select the antenna sensitive to gestures, then use the combined filter for noise reduction and smoothing, and then use the time series difference algorithm to split the data.