## 4 Results and Discussions
### Performance Analysis with Feature-Extracted Data  
As feature extraction ensures the accuracy of the target variables, we have also extracted seven significant features from the TRSP dataset. To compare the performance metrics of the feature-extracted data, we have plotted some of the confusion matrices, ROC curves, and figures here.  
After extracting important features, we achieved 76% accuracy with XGB, whereas accuracy values of 71% and 63% were achieved with RF and KNN, as shown in Figure 5. Moreover, a 76% F1 score and a 76% sensitivity are provided by the XGB. GNB and DT have achieved the minimum scores in this case. In Figure 6 of the extracted data, we have presented several multiclass ROC curves of the five classifiers. In the case of XGB and GNB, class SE and LF show better results as the area under the curve is more than the others. FB indicates a higher area under the curve for DT as well as KNN. Finally Figure 7 represents the confusion matrix of XGB classifier.  
Figure 5: Performance metrics for XGB, RF, DT, KNN, and GNB classifiers.  
Figure 6: 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 for feature-extracted data.  
In comparison with the previous works using the same dataset and some related datasets, the table shows the different values of the performance metrics of those studies as well as ours. We have compared the performance metrics of the related works with our research and found out the limitations of our work.  
The TRSP dataset is imbalanced and we had some challenges while pre-processing the dataset. However, identification of the imbalanced data during the training period and working on the dataset by randomly selecting the amount of data for each element helps to avoid the possible issues of an imbalanced dataset.  
These issues have become a great obstacle to obtaining higher accuracy as well as a higher F1 score in our research work. Moreover, we have extracted seven significant features and compared the results of the raw data and the feature-extracted data. Though the accuracy of the feature-extracted data has decreased a little bit, we intended to analyze the results with all of the classifiers.  
Finally, we have compared our performance metrics with the previous works conducted with the TRSP dataset and evaluated our study. Table 5 shows the values of the different metrics of the research.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Work** & **Dataset** & **Used Model** & **Acc (\%)** & **AUC (\%)** & **F1 Score (\%)** & **Precision (\%)** & **Recall (\%)** \\ \hline
[10] & TRSP & SVM & – & – & 94 & – & – \\ \hline
[9] & TRSP & Transformer & – & – & 87 & 86 & 88 \\ \hline
[11] & TRSP & SVM & – & 80 & 87 & 86 & 88 \\ \cline{2-7}  & RNN & – & 62 & 88 & 87 & 90 \\ \hline
[11] & TRSP & – & – & 51 & – & – & – \\ \hline
[19] & PDD & SVM & & & 98 & 98 & 98 \\ \hline
[20] & PDD & KNN & 98 & & 98 & 98 & 98 \\ \cline{2-7}  & SVM & 98 & & 98 & 98 & 98 \\ \hline Our study & TRSP & XGB & 92 & – & 92 & – & – \\ \hline \hline \end{tabular}
\end{table}
Table 5: A comparison of our work with the related works.  
Figure 7: Confusion matrix of 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 for feature-extracted data.  
As the PDD dataset is a different dataset, 98% accuracy has been achieved with this. In the case of the TRSP dataset, the highest accuracy of 92% was obtained with the XGB classifier, whereas others used SVM, KNN, or RNN models. A maximum of 94% for the F1 score has been achieved with the SVM classifier in [10] and we have obtained 92% in our work. So, the model performances on the dataset are much better in this case to detect compensatory movements.