## 4 Validation and Evaluation
### Experimental Results Based on StanWiFi Dataset  
The StanWiFi dataset was at LOS conditions with a distance of 3 m between Tx and Rx. Rx was equipped with a commercial Intel 5300 NIC with a sampling rate of 1 khz. The accuracy and loss functions of the SM-TCNNET model compared with the LSTM [25] model are shown in Figure 13.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multicolumn{4}{c}{**Fold**} & \multirow{2}{*}{**Average**} \\ \cline{2-2} \cline{4-6}  & **1 st** & **2 nd** & **3 rd** & **4 th** \\ \hline Accuracy & 100.00 & 99.82 & 99.89 & 100.00 & 99.93 \\ Precision & 100.00 & 99.84 & 99.85 & 100.00 & 99.93 \\ Recall & 100.00 & 99.82 & 99.81 & 100.00 & 99.91 \\ F1-score & 100.00 & 99.82 & 99.83 & 100.00 & 99.91 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Indicators under LOS.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multicolumn{4}{c}{**Fold**} & \multirow{2}{*}{**Average**} \\ \cline{2-2} \cline{4-6}  & **1 st** & **2 nd** & **3 rd** & **4 th** \\ \hline Accuracy & 97.57 & 97.76 & 98.28 & 96.63 & 97.56 \\ Precision & 97.44 & 97.65 & 98.19 & 96.45 & 97.43 \\ Recall & 97.45 & 97.55 & 98.21 & 96.45 & 97.42 \\ F1-score & 97.43 & 97.60 & 98.20 & 96.45 & 97.42 \\ \hline \Figure 11: Confusion matrix in the LOS case.  
Figure 12: Confusion matrix in the NLOS case.  
The comparison of the SM-TCNNET model on the StanWiFi dataset with other works in addition to the LSTM model is shown in Table 3. The experiments using this model on the large-scale dataset show that it had high performance metrics and was more competitive with respect to other models.  
This paper presents the performance of the SM-TCNNET model on the StanWiFi dataset with the confusion matrix shown in Figure 14. The confusion matrix showed that a standing action sample was misclassified, probably due to not considering the data frames containing motion signals in the data stream, resulting in the model not capturing all features of the motion signals, which affected the classification accuracy of the model. However, as a whole, the model was able to accurately classify various human activities and performed well on this dataset. Notably, the size of the StanWiFi dataset was larger than the self-collected dataset, but the SM-TCNNET model showed ideal performance on both datasets of different sizes, which further demonstrates the strong robustness of the model.  
Figure 13: Model comparison: (**a**) is the accuracy comparison graph; (**b**) is the loss comparison graph.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & \multirow{2}{*}{**Method and Year**} & \multicolumn{5}{c}{**Metrics (\%)**} \\ \cline{3-6}  & & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline [PERSON] et al. [34] & LSTM (2017) & 90.05 & — & — & — \\ [PERSON] et al. [35] & ABLSTM (2018) & 97.30 & — & — & — \\ [PERSON] et al. [36] & CSITime (2022) & 98.00 & 99.16 & 98.87 & 99.01 \\ [PERSON] et al. [37] & LiteHAR (2022) & 93.00 & — & — & — \\ proposed & SM-TCNNET & **99.80** & **99.81** & **99.80** & **99.80** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with other studies.