# WiGR: A Practical Wi-Fi-Based Gesture Recognition System with a Lightweight Few-Shot Network
## 4 Results
### Recognition Performance Evaluation
We can see that traditional machine learning methods, such as WiGeR and WiCatch, and an ordinary convolutional network, such as SignFi, have almost no shift ability when testing samples from a totally new environment, while our proposed WiGR model could achieve an average accuracy of 98% and 88% using the SignFi and CSIDA data sets, respectively, and therefore remarkably outperforms the other methods. * Cross-user evaluation. For the user shift, we evaluated all methods in the same environment to control variables, and then conducted leave-one-user-out cross-validation using CSI traces from different users. In other words, we adopted CSI traces collected by some users as the training set and utilized the CSI traces of the other users as the testing set. Figure 8 shows the results of recognizing new user's gestures, and each gesture has three support samples. From Figure 8, we can see that the cross-user recognition accuracies of WiGeR, WiCatch, and SignFi are no more than 80%, but still better than the cross-environment performance. The reason is that the training data set has abundant user domain information for extracting common features. Our WiGR model achieves state-of-the-art performance with a recognition average recognition accuracy of 92% and 91% using the SignFi and CSIDA data sets, respectively. Compared with the domain-adaptive Siamese-LSTM, our method improves its performance by about 10%, which demonstrates that WiGR alleviates the problem of domain shift effectively by learning transferable knowledge from the training set and using the features extracted from the support samples to recognize gestures. * Cross-location evaluation. For the location shift, we evaluated all the methods in the same environment to control variables, and then performed leave-one-location-out cross validation using CSI traces. As shown in Figure 9, our proposed WiGR model still shows excellent performance with an average recognition accuracy 90.8%, and, therefore, outperforms other methods. In addition, when the testing CSI data are collected at Loc. 1 and Loc. 3, the recognition performance is slightly reduced compared with the data collected at Loc. 2. This is because that the user performed gestures at Loc. 1 or Loc. 3 is very close to Rx or Tx. In this case, the user's body will block more signals, resulting in weaker signal propagation, which in turn affects gesture recognition performance. Different users have different physical body conditions, gesture speeds, and hand movements for the same gestures, and there are two different layout environments. Moreover, different locations can result in different signal propagation paths. These three factors may result in different CSI signal patterns, even for the same gesture. However, due to the excellent feature extraction capabilities of the CNN, CNN-based gesture recognition systems (i.e., WiGR and Siamese-LSTM) have superior cross-domain recognition performance compared to other gesture recognition systems based on traditional machine learning methods (i.e., WiGeR and WiCatch). Although SignFi also adopts the components of a \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Number of Training Types**} & \multicolumn{2}{c}{**Number of New Testing Types**} \\ \cline{3-3}  & & **2** & **3** \\ \hline Siamese Network [18] & & 65.0\% & 63.4\% \\ Matching Network [20] & & 60.2\% & 55.4\% \\ Prototype Network [21] & & 63.6\% & 58.4\% \\ Relation Network [22] & & 77.4\% & 74.6\% \\ WiGR (fac = 1/6) (ours) & & 89.9\% & 83.6\% \\ WiGR (fac = 1/4) (ours) & & 91.4\% & 85.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy of recognizing new types of gestures with three samples using the CSIDA data set. CNN, the structure of SignFi is too simple to play a role in cross-domain recognition.