# Location Adaptive Motion Recognition Based on Wi-Fi Feature Enhancement
## 5 Experiments and Analysis
### Generalization Experiment  
To verify the generalization of our method, we conduct experiments in different positions, orientations and scenarios, employ 9300 network cards and the PicoScenes software platform for data acquisition and processing, and evaluate the performances of using human velocity distribution compared with that of using original CSI data.  
In the area of 2 m \(\times\) 2 m, the positions of the transmitter and receiver are fixed as shown in Figure 9. The variation interval of the human body position is [u,w,x,y,z], and the variation interval of the human body orientation is [1,2,3,4,5].  
The direction of the human body is toward 4, and the position of the human body is changed in x, y, u, w, and z. Comparing Figure 10a with Figure 10b, we can see that the recognition accuracy of using the human velocity distribution data is much higher than that of using the original CSI data, and the accuracy is even improved by about 14% on average. When the position of the human body changes from the sender to the receiver, the average accuracy of different actions in any position using the human velocity distribution data is above 90%. This indicates that the model has good generalization performance in the location domain.  
The different orientation of the human body will also have a certain impact on the recognition. The human position is fixed as u, and the human orientation is varied in orientations 1, 2, 3, 4, and 5 of Figure 9. Figure 11a shows the action recognition results of the original CSI as input under different orientations, and the average accuracy is 68.2%. We can also find that when the orientation changes, the accuracy of the model also changes. It is mainly because the path of the Wi-Fi signal obeys the law of reflection. In some human body orientations, part of the action reflection signals are occluded by the body, which leads to less action influence in the signal received by the receiver than the normal situation and then leads to the reduction in recognition accuracy. Figure 11b illustrates the accuracy of action recognition under different orientations with human  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **This Paper** & **Widar 3.0** & **EI** & **CARM** \\ \hline F1 Score & 90.24 & 88.79 & 92.32 & 70.45 \\ Precision & 91.65 & 88.12 & 92.43 & 70.60 \\ Recall/TPR & 90.33 & 89.48 & 92.21 & 70.32 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of recognition accuracy of all models in WiAR.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **This Paper** & **Widar 3.0** & **EI** & **CARM** \\ \hline F1 Score & 92.98 & 91.79 & 91.27 & 80.41 \\ Precision & 90.65 & 91.12 & 91.22 & 80.60 \\ Recall/TPR & 91.33 & 92.48 & 91.34 & 80.32 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of recognition accuracy of all models in WiDAR.  
Figure 9: Map of the device and the human body position.  
velocity distribution as input. The average accuracy in this case is 84.4%. In Figure 11b, recognition accuracy is improved under different orientations, indicating that the model has certain generalization performance in the orientation domain. However, in the directions which are easily occluded by the body (orientations 1 and 2), the model cannot eliminate the influence caused by the signal reflection law.  
We also test the algorithm in different scenarios, as shown in Figure 12, among which the training accuracy rate of the open indoor scene is the highest, and the accuracy rate of the office scene is the lowest. Due to the complex environment, the wavelet transform cannot completely remove the noise, which interferes with the performance of the action recognition. but the accuracv rate is still above 84%.  
Figure 11: Accuracy of motion recognition under different orientations. (**a**) Using CSI data. (**b**) Using human velocity distribution.  
Figure 10: Accuracy of motion recognition in different positions. (**a**) Using CSI data. (**b**) Using human velocity distribution.