## 4 Results
### Transformed and Selected Features and Classification  
With the key anatomical points representing body joint positions relevant to trunk postures provided by MoveNet, we further obtained features like angles between segments that might be better related to trunk posture. We computed the Chi-Square Test scores for our calculated features, as shown in Figure 3. We selected several features with higher scores as representative input to models. Based on our experiments, it was proved that features such as the normalized planar angle between nose and hips, shoulders and hips, and the normalized distance between ears and ankles, hips and ankles, etc. were most helpful for model classification. We set a score threshold of 2.5, the 10 most significant features were selected for subsequent classification models. To gain a deeper understanding of the relationship between the selected 10 features and the targeted class labels, we generated violin plots for these selected features, as shown in Figure 4. Violin plots summarize statistics and density shape into a single plot, which provides an insightful visualization of the distribution of feature values concerning the class labels [48]. From the violin plots, we can see the frequency distribution of different eigenvalues. Also, the shapes of the violin plots listed in the figure were different between the three classes. In Figure 4, the violin plots are only shown for the normalized distance between ankles and hips, the normalized distance between shoulders and knees, the normalized planar angle between nose and hips and the normalized planar angle between nose and knees. The violin plots for the distance between ears and ankles exhibited a shape  
Figure 3: Bar chart showing chi-Square test scores (vertical axis) of all calculated features (horizontal axis), along with the cut-off score in the red line. akin to that of the distance between hips and ankles. Additionally, the planar angle between nose and hips, eyes and hips, shoulders and hips, ears and hips also shared a resemblance, indicating similar feature distributions. We did not delete similar features because some key parts (such as ears) would have been occluded in some agricultural activity pictures, particularly those captured from sideways angles or with individuals bent over. With the nonlinear model that account for the selected features together like ANN, we could hopefully complete the classification task with better performance. We put the calculated features into the model and compared the result with applying the landmarks from Movenet directly. The new results are shown in Table 3 and confusion matrix in Figure 5. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Pre-Trained Models**} & \multicolumn{2}{c}{**Training Results**} & \multicolumn{3}{c}{**Test Results**} \\ \cline{2-6}  & **Accuracy** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline SVM & 85.47\% & 80.48\% & 81.78\% & 80.48\% & 80.83\% \\ \hline DT & 98.29\% & 78.57\% & 79.41\% & 78.57\% & 78.75\% \\ \hline RF & 98.29\% & 85.36\% & 87.92\% & 85.36\% & 86.53\% \\ \hline ANN & 94.44\% & 87.80\% & 87.46\% & 87.52\% & 87.41\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance metrics of the classification using new features. Figure 4: Violin plots for selected normalized features separated by trunk postures: (**a**) distance between hip and ankle, (**b**) distance between shoulder and ankle, (**c**) angle between the position of nose and hip, and (**d**) angle between the position of nose and knee. With the calculated and selected features that were most relevant to trunk bending, the model's test accuracy and F1 performance improved by almost 10%. From the results of the confusion matrix, it can be observed that the cases where predictions did not match the labels were primarily associated with the 'half bend' one, while 'fully bend' and 'neutral' were less prone to misclassification.