## 4 Performance Validation
In order to report the enhanced performance of the QWSA-HDLAR model, a comparison study with existing methods [25; 26; 27] is performed on the KTH dataset in Table 4. The results implied that the Gaussian mixture model with Kalman filter (GMM-KF) model has shown poor performance with a lower \(accu_{y}\) of 90.47%, whereas the gated recurrent neural network (GRNN) model has attained a slightly enhanced \(accu_{y}\) of 85.85%. This is followed by the support vector machine with 3 DCNN (SVM-3 DCNN) and the CNN with convolutional autoencoder (CNN-CAE) models, which obtained improved \(accu_{y}\) values of 90.45% and 92.80%, respectively. Though the GMM-KFGRNN and SDL-HBC models resulted in reasonable \(accu_{y}\) values of 95.52% and 99.38%, the QWSA-HDLAR model gained a higher \(accu_{y}\) of 99.63%. \begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Accuracy (\%)** \\ \hline QWSA-HDLAR & 99.63 \\ GMM-KF & 70.47 \\ GRNN & 85.85 \\ GMM-KFGRNN & 95.52 \\ SDL-HBC model & 99.38 \\ SVM-3 DCNN & 90.45 \\ CNN-CAE & 92.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative \(accu_{y}\) analysis of QWSA-HDLAR model on KTH dataset. Figure 4: Training and validation accuracies of QWSA-HDLAR model on KTH dataset. Figure 6 establishes the confusion matrices produced by the QWSA-HDLAR approach on the UCF Sports dataset. The figure implied that the QWSA-HDLAR technique has proficiently identified all ten class labels effectually on the applied data. Figure 5: Training and validation losses of QWSA-HDLAR model on KTH dataset. Figure 6: _Cont._  
Table 5 exemplifies the overall HAR outcomes of the QWSA-HDLAR technique on the test UCF Sports dataset. The experimental output illustrated that the QWSA-HDLAR approach showed enhanced performance on all datasets. For example, on the entire dataset, the QWSA-HDLAR algorithm achieved average \(accu_{y}\) of 99.06%, \(reca_{l}\) of 95.30%, \(spec_{y}\) of 99.48%, \(F_{score}\) of 95.30%, and AUROC score of 97.39%. Finally, with 70% of the TR dataset, the QWSA-HDLAR approach reached average \(accu_{y}\) of 99.06%, \(reca_{l}\) of 95.40%, \(spec_{y}\) of 99.48%, \(F_{score}\) of 95.28%, and AUROC score of 97.44%. At the same time, on 30% of the TS dataset, the QWSA-HDLAR methodology reached average \(accu_{y}\) of 99.07%, \(reca_{l}\) of 95.26%, \(spec_{y}\) of 99.48%, \(F_{score}\) of 95.13%, and AUROC score of 97.37%. The training and validation accuracies depicted by the QWSA-HDLAR methodology with distinct epochs on UCF Sports dataset are demonstrated in >Figure 7. The results ensured that the accuracies were higher with increased epochs. In addition to that, training accuracy seems to be better than testing accuracy. Figure 6: Confusion matrix of QWSA-HDLAR model on UCF Sport dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class Labels** & **Accuracy** & **Recall** & **Specificity** & **F-Score** & **AUROC Score** \\ \hline \multicolumn{6}{c}{Entire Dataset} \\ \hline
1 & 98.40 & 92.00 & 99.11 & 92.00 & 95.56 \\
2 & 98.90 & 92.00 & 99.67 & 94.36 & 95.83 \\
3 & 98.90 & 96.00 & 99.22 & 94.58 & 97.61 \\
4 & 99.60 & 99.00 & 99.67 & 98.02 & 99.33 \\
5 & 99.30 & 97.00 & 99.56 & 96.52 & 98.28 \\
6 & 98.80 & 97.00 & 99.00 & 94.17 & 98.00 \\
7 & 99.00 & 93.00 & 99.67 & 94.90 & 96.33 \\
8 & 99.00 & 95.00 & 99.44 & 95.00 & 97.22 \\
9 & 99.30 & 95.00 & 99.78 & 96.45 & 97.39 \\
10 & 99.40 & 97.00 & 99.67 & 97.00 & 98.33 \\ \hline Average & 99.06 & 95.30 & 99.48 & 95.30 & 97.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: HAR outcomes of QWSA-HDLAR model on UCF Sport dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class Labels** & **Accuracy** & **Recall** & **Specificity** & **F-Score** & **AUROC Score** \\ \hline \multicolumn{6}{c}{Training Phase (70\%)} \\ \hline  
\hline Average & 99.07 & 95.26 & 99.48 & 95.13 & 97.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._  
Figure 7: Training and validation accuracies of QWSA-HDLAR model on UCF Sport dataset.