## 3 Results
### Detection of Improper Body Posture  
The craniovertebral angle (CVA) serves as a widely recognized measure for evaluating forward head posture (FHP). FHP is characterized by a CVA measurement below the threshold of 48-50\({}^{\circ}\). It is noteworthy that the degree of FHP severity escalates with a reduction in CVA. Hence, there exists an inverse relationship between FHP and CVA magnitude, indicating that FHP tends to increase as CVA diminishes. In our study, 84% of participants exhibited a CVA of less than 50\({}^{\circ}\) while engaged in computer work [37]. This posture imposes a strain on the neck muscles and skeletal structure, potentially leading to muscular imbalances attributable to uneven distribution of muscle load. This is why we considered it important and attempted to detect this condition based on the MMD-MSD dataset.  
Below, we consider the task of improper head position detection (protracheal head posture) in a person-independent scenario, which is further reduced to a two-class classification problem--proper vs. improper head positions. Particularly, we sought the automated detection of improper head position based on postural angles #3 and #4 derived from the MMD-MSD pictures, while the volunteers were in their spontaneous sitting posture. For that purpose, we experimented with two scenarios:  
* _Experiment 1_ (self-reported tags related to neck pain--question #25 with body area 2);
* _Experiment 2_ (tags defined by medical doctors);
* Then, we evaluated the applicability of the classifiers outlined in Section 2.7 using 10-fold cross-validation. Specifically, we present experimental results only for the three best-performing classifiers: naive Bayes with Laplacian correction; decision tree C4.5 with leaf size one and accuracy optimization criterion with max depth 2; and random forest with 20 trees, optimization of the accuracy criterion, and max depth 4.  
In Table 7, we show the average classification accuracy obtained for the two different sets of tags. The best average classification accuracy of 80% (_Experiment 2_) was obtained with random forest. The observed lower detection accuracy for the self-reported tags (_Experiment 1_) can be explained by the fact that most of the participants were young people, with most of them actively involved in sports. Their bodies were fit, which partially compensates for the improper body position and does not immediately result in neck pain. The tags established by the medical doctors (_Experiment 2_) correspond to an improper neck posture that increases the risk of pain in the specific area, even if this pain is not felt chronically or reported yet.  
For the other classifiers, namely, naive Bayes and decision tree C4.5, we observed much lower detection accuracy in both scenarios. We deemed that the lower classification accuracy of the naive Bayes classifier was primarily due to the high correlation and the significant redundancy of information carried by angles #3 and #4, which violated the feature independence assumption implicit in the naive Bayes classifier. The lower accuracy observed for the C4.5 decision tree can be explained by the loss of information forced by choosing either angle #3 or angle #4 as the top-level node. None of these angles is decisive, and this limits the opportunity for making proper decisions at a lower node if an error is made at an upper node.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{2}{c}{**Accuracy \(\pm\) Std [\%]**} \\ \cline{2-3}  & **Self-Reported Tags** & **Tags by Medical Doctors** \\ \hline Random forest & 68.0 \(\pm\) 3.0 & 80.0 \(\pm\) 2.0 \\ Naive Bayes & 60.0 \(\pm\) 2.0 & 70.0 \(\pm\) 1.0 \\ Decision tree C4.5 & 60.0 \(\pm\) 1.0 & 71.0 \(\pm\) 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Averaged accuracy and standard deviation (std) for proper head posture vs. protracheal head posture classification, based on postural angles #3 and #4.