# WiGR: A Practical Wi-Fi-Based Gesture Recognition System with a Lightweight Few-Shot Network
## 1 Introduction  
With the rapid development of the Internet of Things technology, various smart devices have changed people's lives. Human-computer interaction technologies, i.e., information interaction between humans and computers, have become essential. Since gestures have the advantages of easy learning, rich information, and simplicity, gesture recognition technology [1] has become a research hotspot in recent years. Gesture recognition technology can be widely used in virtual games, automatic driving assistance systems, sign language recognition, and intelligent robot control. Currently, the main problems of the existing gesture recognition methods based on wearable sensors [2, 3] and cameras [4, 5] are that they are not convenient enough, the required equipment is expensive, and there is a risk of privacy leakage, which limits the wide application of gesture recognition systems in reality. Gesture recognition technology is more practical than ever before under the booming development of Wi-Fi sensing technologies, progressively transitioning from theoretical research to practical landing application stages due to their advantages of a contactless manner, low cost, good privacy, and the fact that they do not require line-of-sight propagation (LoS) [1]. Specifically, the development of gesture recognition systems is moving from the single domain to the cross domain, from recognizing fixed types of gestures to new types of gestures. In addition, a gesture recognition system is increasingly deployed in the mobile environment, and its model has also transformed from heavyweight to lightweight to meet the requirements of mobile device deployment. Wi-Fi sensing technologies recognize a gesture by analyzing a gesture's feature, extracted from the channel state information (CSI) of Wi-Fi signals, which are generated during the execution of the gesture. A convolution neural network [6; 7; 8], an important neural network model of deep learning, has excellent feature extraction capabilities. Therefore, Wi-Fi-based gesture recognition methods mainly adopt deep learning algorithms to recognize gestures [9; 10; 11; 12]. However, these methods concentrate on single domain recognition. Once they face new types of gestures or gestures performed in a new domain, the recognition performance will dramatically degrade, and a large amount of testing data from the new domain is needed to adjust the model. This problem is called a \"domain shift\" and is a substantial challenge for improving the practicality of the gesture recognition system. In addition, deep-learning-based gesture recognition systems usually have a complex neural network model. Due to the limitations of storage space and computation consumption, the storage and calculation of neural network models on mobile devices are other substantial challenges. Therefore, designing a lightweight gesture recognition system with good recognition performance in the new domain using a small amount of data is an essential aspect of facilitating the application of gesture recognition technology. Recently, there has been an increasing amount of literature adopting the transfer learning technique [13; 14; 15], generative adversarial networks [16], or a manually designed domain-independent feature body-coordinate velocity profile [17] to eliminate the domain shift problem. However, the excellent performance of these methods depends on high amounts of data, and the manual modeling method needs to analyze complex CSI data. Since the influence pattern of gestures on Wi-Fi signals is complicated, the model of velocity profiles is complicated as well. In addition, inspired by the few-shot learning technique [18; 19; 20; 21; 22], [PERSON] et al. [23] and [PERSON] et al. [24] combined a few-shot network and adversarial learning to remove domain-related information. [PERSON] et al. [25] proposed a few-shot multi-task classifier to address the domain shift problem. The basic idea is to initialize the parameters of the classifier so that the classifier can quickly adapt to a new domain.