# Human Activity Recognition Based on Non-Contact Radar Data and Improved PCA Method
## 4 Experiments and Results
### Test and Evaluation  
In order to verify the effectiveness of the proposed algorithm and network structure, the parameters of accuracy, precision, recall, F1-score, and training time were used as the evaluation index of the experiments.  
\[Accuracy=\frac{TP+TN}{TP+FN+FP+TN} \tag{9}\]  
\[Precision=\frac{TP}{TP+FP} \tag{10}\]  
\[Recall=\frac{TP}{TP+FN} \tag{11}\]  
\[F1-score=2\;*\;\frac{Precision\;*\;Recall}{Precision+Recall} \tag{12}\]  
TP means true positive, TN means true negative, FN means false negative, and FP means false positive.  
Comparative experiments were conducted to evaluate the performance of our proposed method with traditional methods. The spectrograms in the dataset were divided into the training set and test set by a ratio of 8:2. The initial learning rate is \(1\times 10^{-4}\), batch size is 32, and the epoch is 300. The optimizer adopted Adam and the loss function was the cross-entropy loss function.  
The following methods were trained respectively:  
* Method 1: Training raw radar spectrograms through VGG16;
* Method 2: Training raw radar spectrograms through improved VGG16;
* Method 3: Training the data processed by traditional PCA through improved VGG16;  
Figure 9: Comparison of spectrograms processed by the improved PCA and the traditional PCA.  
* Proposed Method: Training the data processed by improved PCA through improved VGG16.  
The training accuracy of these four methods with epochs based on the training set is shown in Figure 10.  
The accuracy, precision, recall, and training time of each round of the four methods based on the test set are shown in Table 3.  
As shown in Table 3, method 1 used raw radar spectrograms to train the VGG16 model with 90% accuracy after 100 rounds of training; At this point, the parameters had converged in a smaller range. However, the curve appeared to oscillate after 100 rounds and there was over-fitting as the dataset is small-scale. Method 2 used raw radar spectrograms to train the improved VGG16 model, achieving 90% accuracy after 50 rounds, 1.4% higher than method 1. Although method 2 is faster, there was still over-fitting in the later training phase. Method 3 used samples processed by traditional PCA to train the improved VGG16 model. These reconstructed samples removed surplus information from the raw radar spectrograms and compressed the data dimensions. The results of method 3 showed that although the model can converge in less than 50 rounds, its accuracy and precision are the lowest among the four methods. Its accuracy-epoch curve also had a severe oscillation later in training, which usually indicates serious over-fitting. The proposed method used processed images which were given by the improved PCA to train the improved VGG16 model, with the results showing that it had the best performance among the four methods in terms of accuracy, precision, recall, and F1-score. The proposed method converged faster than methods 1 and 2. Although it did not converge as fast as method 3, its accuracy-epoch curve was the most  
Figure 10: Accuracy of training for the four methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Accuracy/\%**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F1-Score**} & \multirow{2}{*}{
\begin{tabular}{c} **Training** \\ **Time** \\ **/epoch/s** \\ \end{tabular} } \\  & & & & & \\ \hline Method 1 & 92.07 & 0.93 & 0.91 & 0.92 & 12.47 \\ \hline Method 2 & 93.47 & 0.94 & 0.93 & 0.93 & 11.39 \\ \hline Method 3 & 87.84 & 0.90 & 0.87 & 0.89 & 10.14 \\ \hline Proposed Method & 96.34 & 0.96 & 0.96 & 0.96 & 10.88 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of different methods.  
stable, with no significant oscillations in the later training phase. According to the results, the proposed method had the best performance compared with other methods, and the improved PCA combined with the improved VGG16 model significantly improved the performance of HAR and reduced the training time.