# WiGR: A Practical Wi-Fi-Based Gesture Recognition System with a Lightweight Few-Shot Network
## 1 Introduction
[PERSON] et al. [26] proposed a Siamese recurrent convolutional architecture to remove structured noise and used convolution neural network (CNN)-long short-term memory (LSTM) to extract temporal-spatial features. Although these methods can eliminate the domain shift problem with a small amount of data, they require more computation. Their complex models with many parameters are not suitable for deployment. To address the challenges mentioned above, we proposed WiGR, a novel, practical Wi-Fi-based gesture recognition system. The key structure of WiGR is an improved few-shot learning network, which consists of a feature extraction subnetwork and a similarity discrimination subnetwork. The feature extraction subnetwork adopted a 2-D convolutional kernel [6] to simultaneously extract the spatial features and temporal dynamics of gestures. Similar to the relation network [22], the similarity discrimination subnetwork uses a learning-based neural network as the similarity measurement method to determine the type of gesture, and this is more accurate than using fixed functions as measurement methods [18; 19; 20; 21]. The whole network can learn a transferable similarity evaluation ability from the training set and apply the learned knowledge to the new testing domain via an episode-based training strategy [20] to eliminate the problem of domain shift. In addition, there is evidence that lightweight networks [27; 28; 29; 30; 31] play a crucial role in mobile deployment. Therefore, we introduce depthwise separable convolution and an inverted residual layer of a linear bottleneck [30; 31] in a few-shot learning network to reduce model computations and parameters. Simultaneously, to reduce the complexity of the model while the recognition performance does not decrease accordingly, we introduce a squeeze and excitation (SE) block [32] to improve the quality of the features generated from the network by explicitly modeling the interdependence between the network convolution feature channels. Later extensive experiments on two data sets (CSI-Domain Adaptation (CSIDA) and SignFi [10]) demonstrate that WiGR can achieve excellent recognition per formance in cross-domain evaluation, and our network design dramatically reduces the model computations. Our contributions can be summarized as follows:  
* We designed a novel Wi-Fi-based gesture recognition system called WiGR that is more practical than existing gesture recognition systems. The practicality is reflected in its ability to recognize new gestures or gestures performed in new domains using just a few new samples. * A lightweight few-shot learning network, which consists of a feature extraction sub-network and a similarity discrimination subnetwork, is proposed to address the hard domain shift problem. Lightweight and effective blocks are introduced in the network to achieve lower computational complexity and high performance. * We made a CSIDA data set, which includes CSI traces with various domain factors, to simulate real scenes. The CSIDA data set was helpful for us to verify the accuracy of the proposed WiGR in cross-domain evaluation. * Extensive experiments on the SignFi data set and the CSIDA data set show the superiority of the proposed WiGR over existing gesture recognition systems in terms of cross-domain accuracy and computational complexity.