## 5 Conclusions  
In this paper, we propose an algorithm for classifying sitting postures during desktop computer work into the categories of good posture, text neck posture, and L-posture. The proposed method in this paper uses the skeleton algorithm to obtain the 'head-neck', 'neck-chest', and 'chest-hip' angles from the lateral view image data of the user captured by a camera. Then, these angles are used to classify the three postures. The proposed method can judge postures from images freely as long as there is space for lateral shooting, and it can classify postures at a low cost since it can be applied not only to a CCD camera but also to images taken with a smartphone. Moreover, the proposed method has an advantage in that it can classify not only the text neck but also the L-posture, which is a posture that adversely affects the spine. Because of this reason, the research proposed in this paper can contribute to the study of classifying text neck posture and L-posture together, which has not been conducted before. The classification accuracy of the experimental results was 97.0588%, and in particular, the L-posture was classified with 100% accuracy. This is considered to be due to the fact that the proposed algorithm reflects the characteristics of each posture angle and was able to achieve high accuracy. However, there was about 3% misclassification, and some of them were images that were difficult to classify accurately between the good posture and the text neck posture. Furthermore, some of the subjects showed symptoms of text neck, which resulted in misclassification. In the comparison experiment, we assessed whether conventional research methods could classify and measure good posture, text neck posture, and L-posture. However, the method using three-axis accelerometer sensors failed to classify L-posture, while the method using a sensor cushion failed to classify text neck posture. Therefore, the experiments using conventional research methods demonstrated limitations in classifying text neck posture and L-posture, which are detrimental postures of the neck and spine. In future studies, we plan to further refine the classification of the text neck posture to overcome the limitations of misclassification identified in this study. In addition, we plan to add more postures such as leaning to the side or crossing legs and develop a system for real-time posture correction based on the results. To reduce misclassification, we will incorporate an initial assessment to identify participants who exhibit text neck syndrome. In this study, the proposed method, as mentioned in Figure 8 of Section 4, classified participants as having a forward head posture despite their attempt to maintain a good posture, demonstrating the ability to classify individuals with text neck symptoms. We will utilize this approach to incorporate an initial assessment in the future development of a real-time posture correction system. Conceptualization, J.L., H.C., K.Y. and J.K.; methodology, J.L. H.C., K.Y. and J.K.; software, J.L., H.C. and J.K.; validation, J.L., H.C., K.Y. and J.K.; formal analysis, J.L., H.C. and J.K.; investigation, J.L., H.C. and J.K.; resources, J.L., H.C. and J.K.; data curation, J.L., H.C. and J.K.; writing--original draft preparation, J.L. ; writing--review and editing, K.Y. and J.K.; visualization, J.L., H.C. and J.K.; supervision, J.K.; project administration, J.K.; funding acquisition, J.K. All authors have read and agreed to the published version of the manuscript. This research was funded by National Research Foundation of Korea, grant number CD202112500001, Small and Medium Business Technology Innovation Development Project from TIPA, grant number 00220304 and Link 3.0 of PKNU, grant number 202310810001. The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of University of Dong-Eui Institutional Review Board (DIRB-202303-HR-E-42 and 6 March 2023). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.