# WiGR: A Practical Wi-Fi-Based Gesture Recognition System with a Lightweight Few-Shot Network
## 4 Results
### Recognition Performance Evaluation  
Recognizing new types of gestures. The ability to recognize new types of gestures is important for enhancing the scalability of a gesture recognition system. The few-shot learning method, the key technology used in this paper, can realize the generalization of the model through just a few support samples. This is the key difference between the few-shot learning method and other domain adaptation methods. To verify the ability of the proposed system to identify new types of gestures through just a few samples, we compared it with other few-shot learning methods on the SignFi data set and the CSIDA data set. Table 5 demonstrates that the WiGR model can achieve 98.6%, 97.2%, and 95.8% accuracy when it recognizes 10, 20, and 30 new types of gestures, respectively, under the condition that 100 new types of gestures are used for training and each new gesture has three support samples. Compared with other methods, the improvement in accuracy is more than 10 percentage points. Table 6 demonstrates that our WiGR model has better recognition performance than the other few-shot learning models. When WiGR was trained with three old types of gestures, it achieved 91.4% and 84.9% recognition accuracies for two and three new types of gestures, and each new type of gesture had three support samples. Because our CSIDA data set does not have enough training types for training, the recognition accuracy dropped slightly compared with the SignFi data set. In general, the accuracy of the proposed WiGR model is remarkably higher than the other few-shot learning models in all evaluations. Cross-domain evaluation. To verify that the proposed WiGR system does play a role in cross-domain recognition, we conducted extensive cross-domain experiments by splitting the data set according to the layout of the environment, the user who performs the gestures, and the user's location. We compared our model with other traditional gesture recognition systems, such as WiGeR [33], which utilizes a classifier with a DTW algorithm, and WiCatch [34], which employs SVM with a MUSIC signal processing algorithm. In addition, since the proposed WiGR adopts components of a CNN to construct a network, then, to verify the superiority of the CNN-based WiGR, the selected comparison systems were based on machine learning algorithms (i.e., WiGeR and WiCatch) or based on only a sample structure of a CNN without the capability of cross-domain recognition (i.e., SignFi [10]). Moreover, Siamese-LSTM [26], using a Siamese network that consists of a CNN and LSTM to address domain shift problems, is a typical few-shot domain adaptive method and was used as a baseline method. These competitive methods were useful in verifying the effectiveness of our WiGR model in cross-domain evaluation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Number of Training Types**} & \multicolumn{3}{c}{**Number of New Testing Types**} \\ \cline{3-5}  & & **10** & **20** & **30** \\ \hline Siamese Network [18] & & 84.1\% & 85.2\% & 79.5\% \\ Matching Network [20] & & 78.4\% & 73.2\% & 69.9\% \\ Prototype Network [21] & & 76.6\% & 71.5\% & 67.8\% \\ Relation Network [22] & & 89.2\% & 85.5\% & 81.4\% \\ WiGR (fac = 1/6) (ours) & & 98.2\% & 96.8\% & 93.0\% \\ WiGR (fac = 1/4) (ours) & & 98.6\% & 97.2\% & 95.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy of recognizing new types of gestures with three samples using the SignFi data set. * Cross-environment evaluation. For the environmental shift, we used CSI data from two different environments. All the data from one environment were used for training, while data from the other environment were used for testing. Figure 7 shows the accuracy for recognizing gestures that are collected in a new environment with three support samples for each gesture, where A \(\rightarrow\) B denotes that A is the training set and B is the testing set.