## 4 Results and Discussions
### Performance Analysis with Raw Data  
We have made a comparison between the classifiers' performances. Table 4 represents the various results of the classifiers on the raw dataset and we have made a comparison among the performance metrics. We can compare the values of accuracy, F1 scores, FPR scores, and so on from the table. Moreover, we have made a comparison between the confusion matrix and ROC curves for the following classifiers. We have also represented the multiclass curves to show the interrelations between the classes and evaluate them. From Table 4, representing performance metrics, we have obtained a maximum accuracy value of 92% with XGB, 88% with RF, and the rest follow behind these values. Meanwhile, the F1 score with XGB is 92%, with RF, it is 88%, and with KNN, it is 77%. XGB has achieved the best accuracy among them and that is why we have selected XGB for our work on the TRSP dataset.  
The multiclass ROC curves are represented in Figure 3, where the six different curves represent six classifiers. They are multiclass as they have six classes representing six different exercises. Each color indicates each class versus the rest of the classes. The classes or exercises are labeled as forward-backward (FB), side-to-side (SS), lean forward (LF),  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifiers** & **Acc (\%)** & **F1 Score** & **FPR Score** & **Sensitivity** & **Specificity** \\ \hline XGB & 92.45\% & 92.3555 & 1.50\% & 92.45\% & 98.49\% \\ \hline RF & 88.82\% & 88.52\% & 2.24\% & 88.82\% & 97.75\% \\ \hline DT & 63.68\% & 64.24\% & 7.23\% & 63.68\% & 92.76\% \\ \hline KNN & 78.21\% & 77.56\% & 4.50\% & 78.21\% & 95.49\% \\ \hline GNB & 51.95\% & 51.26\% & 9.68\% & 51.95\% & 90.31\% \\ \hline \hline \end{tabular}  
* Acc = accuracy.  
\end{table}
Table 4: The performance metrics for the XGB, RF, DT, KNN, and GNB classifiers.  
shoulder elevation, forward-backward trunk rotation (FB_TR), and side-to-side trunk rotation (SS_TR).  
The area under the curve (AUC) shows the performance of the classification, so a larger AUC is better. In the case of XGB, FB and SS show a better performance compared to the others but the difference between them is not much, whereas LF shows better results with DT and GNB.  
The confusion matrix represents the efficacy of a binary or categorical classifier and visualizes the possible errors that can be made by the classifiers. For better visualization of the recall, precision, and ROC curve, the confusion matrix helps a lot. The diagonal elements indicate the proper labels, whereas the off-diagonal elements show the misclassified elements. There may occur two types of errors, namely false positive and false negative. False positive refers to an actual negative value being predicted as positive and false negative means predicting the positive value as negative. In the two particular cases, both of them are wrong. Hence, Figure 4 represents the confusion matrices of the raw dataset:  
Figure 4: Confusion matrix of the XGB classifier for the forward–backward (FB), side-to-side (SS), lean forward (LF), shoulder elevation, forward–backward trunk rotation (FB_TR), and side-to-side trunk rotation (SS_TR) exercises.  
Figure 3: Multiclass ROC curve for XGB classifier for forward–backward (FB), side-to-side (SS), lean forward (LF), shoulder elevation, forward–backward trunk rotation (FB_TR), and side-to-side trunk rotation (SS_TR) exercises.