## 4 Experiment Results and Discussion
### Experiment Results  
First, we utilized a skeleton algorithm to detect the positions of the head, neck, chest, and hips in the participants' image data. Then, based on the results, we measured \(\theta_{1}\), \(\theta_{2}\), \(\theta_{3}\), and \(\theta_{4}\) for each posture and calculated them by using Equations (2) and (3). The results are shown in Table 1 and Figure 6. Table 1 presents the average, minimum, maximum, and standard deviation values of each angle for each posture, including good posture, text neck posture, and L-posture. Figure 6 shows box plots for the angles of each posture. In Table 1, the mean values of the angles \(\theta_{2}\) and \(\theta_{3}\) in the L-posture were 118.1552 and 126.6295, respectively. In contrast, for the good posture, the mean values were 95.6220 and 100.3043, respectively, and for the forward head posture, they were 79.4418 and 86.1497, respectively. Through this, it was found that the angle values in the L-posture were larger than those in other postures, and this difference was confirmed in Figure 6. Therefore, it was possible to determine whether the posture was an L-shaped posture by using the angle values between the neck and chest, and between the chest and hips. In addition, the average values of the \(\theta_{1}\) and \(\theta_{4}\) angles were 79.6379 and 86.9680, respectively, in the good posture, while they were 56.1264 and 66.7944 in the turtle-neck posture. Furthermore, the minimum and maximum values of \(\theta_{1}\) were 68.1986 and 90.0000, respectively, in the good posture, and the minimum and maximum values of \(\theta_{4}\) were 78.9390 and 97.2055, respectively. On the other hand, when in text neck posture, the minimum and maximum values of \(\theta_{1}\) were 30.9638 and 68.1986, respectively, while those of \(\theta_{4}\) were 46.5310 and 79.1811. This indicates that in the text neck posture, the upper body is tilted forward, and the neck protrudes forward more compared to the good posture, resulting in a difference in the angles of \(\theta_{1}\) and \(\theta_{4}\). Based on the experimental results, we set the threshold values used in the proposed algorithm to Th\({}_{1}\) = 105, Th\({}_{2}\) = 110, Th\({}_{3}\) = 70, and Th\({}_{4}\) = 80, respectively. Using the previously defined threshold values, we applied the proposed algorithm to classify the three postures. As a result, the classification accuracy of the proposed algorithm was 97.0588%, with an F1-score of 95.23%. Figure 7 shows the confusion matrix for the classification results of the three postures. As shown in Figure 7, all images of the L-posture were accurately classified, with a true value ratio of 100%. The good posture and text neck posture had accuracy rates of 93.00% and 98.09%, respectively. The percentage of misclassifying the actual class as a text neck posture when it was a good posture was 7.00%, and the percentage of misclassifying the actual class as a good posture when it was a text neck posture was 1.91%. Table 2 shows the precision and recall results for each posture. As can be seen in Table 2, all values for L-posture were 100%. When in a good posture, the precision and recall were 97.55% and 93.00%, respectively, while for a text neck posture, the precision and recall were 94.47% and 98.09%, respectively. On the other hand, all misclassification occurred because it could not distinguish between the good posture