## 5 Experimental Results
### Performance Evaluations  
We calculate classification accuracies for emphasizing the effectiveness and efficiency of the proposed system using Equation (10) for each action. \[\text{Accuracy}(\%)=\frac{\text{Total Correctly Predicted Observations}}{\text{Total Number Observations}}\times 100 \tag{10}\]  
We carry out the evaluation process in six different ways to provide more clarification about the discriminability of human activity using the proposed method. We initially train the deep learning models with three different views along \(XY\), \(YZ\), and \(ZX\) planes separately and compute the test results. Then the features extracted from three different views are fused in three different ways and enumerate the classification results. We conduct three well-known deep learning models including MobileNetV2, DenseNet121, and ResNet18 to represent the robustness of the proposed system. Table 2 enlists human activity recognition results with different configurations of data and models using the UTD-MHAD dataset. The proposed method achieves classification accuracies about 97.29% and 98.21% for DenseNet121 using front and top views of the UTD-MHAD dataset respectively. However, while we apply side view data, MobileNetV2 (96.06%) shows better performance than DenseNet121 (95.98%) and ResNet18 (93.17%). As we mentioned in the previous discussion, we fuse the features obtained from the front, side, and top views dataset using three techniques to improve the classification accuracies in MobileNetV2, DenseNet121, and ResNet18. While fusing the features by applying the conventional average and maximization fusion techniques, the proposed method gets 98.51% and 99.65% highest classification accuracies using DeNseNet121. The element-wise multiplication of the features provides 98.93% discrimination accuracies using MobileNetV2. By considering the recognition results in Table 2, it is clear that DenseNet121 outperforms both MobileNetV2 and ResNet18 because it can generate deeper features from the STIF representation of the skeleton data. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Hardware & Intel (R) Core(TM) i7 CPU, GeForce GTX 1080 GPU \\ Software & Windows 10, Linux 16.04, MATLAB 2019b, Python 3.5 \\ Initial learning rate & 0.001 \\ Learning rate dropping factor & 0.9 \\ Learning rate dropping period & 10 \\ Optimizer & Adam \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hardware, software, and parameters configurations. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **MobileNetV2** & **DenseNet121** & **ResNet18** \\ \hline Front view (\(XY\) plane) & 96.52\% & 97.29\% & 95.29\% \\ Side view (\(YZ\) plane) & 96.06\% & 95.98\% & 93.17\% \\ Top view (\(ZX\) plane) & 97.08\% & 98.21\% & 94.67\% \\ \hline Fusion (\(f_{avg}\)) & 97.98\% & 98. We again investigate the performance of human activity recognition on the MSR-Action3D dataset using MobileNetV2, DenseNet121, and ResNet18 for showing the robustness of the proposed method. Table 3 shows the human action recognition results on the MSR-Action3D dataset. Like the UTD-MHAD dataset, we obtain better accuracies of about 94.83% for DenseNet121 compared with MobileNetV2 (93.83%) and ResNet18 (93.04%) on the front view dataset. A similar trend appears in the case of the top view dataset in which DenseNet121 classifies human action with an accuracy of about 93.00% while MobileNetV2 and ResNet18 secure accuracies of about 91.67% and 92.08% respectively. ResNet18 obtains the highest accuracy about 97.50% using average fusion on the MSR-Action3D dataset. For both the multiplication and maximization fusions, DenseNet121 secures about 96.67% and 98.75% discrimination results. To render more clarification about the performance of the proposed method for each backbone model along with each experiment, we visualize the graphical results shown in Figure 10a,b. From the Figure 10, we can argue that DenseNet121 works much better in comparison with MobileNetV2 and ResNet18 for both UTD-MHAD and MSR-Action3D datasets.