# Classification of Children's Sitting Postures Using Machine Learning Algorithms
## 6 Conclusions  
In this study, we developed a real-time sitting posture classification system for the correct sitting postural habit formation for children. Five sitting postures were selected for classification. The CNN algorithm performs well in this type of posture classification and has been shown to outperform other machine learning algorithms. In addition, as compared to previous studies, the average value of accuracy (0.953) for the sitting posture obtained using CNN was confirmed to be suitable for the development of a smart chair. As a result of examining the accuracy of the posture classification by the weight of the children, it was found that the accuracy is higher if the weight of the group is higher. This result could not be generalized owing to the limitation of this study being that the number of participants in the weight group is small. Thus, further research will be conducted to develop a sensing cushion that has an optimal accuracy for each weight group by adjusting the position or number of sensors. Because children have a higher correlation between body shape and body weight than adults, there is a difference in the active area of the pressure sensor depending on body weight. In addition, there are cases where the different sitting postures represent the same pressure pattern because the difference in body size is large depending on the body weight. For example, the pressure sensor pattern might be similar when a child in a small body size is sitting upright in a chair and when a large child is sitting in front of a chair. In this study, the pressure sensor mat which occupies most of the seat area is used, and the position of the individual pressure sensor is arranged at equal intervals. However, to overcome the misclassification due to the body characteristics according to the weight, further studies are required to discover the optimum pressure sensor position and number. Preciselyclassifying the sitting posture when using a chair helps prevent the occurrence of musculoskeletal disorders in users and improves their postural habit as well as efficiency in work or study. This study is expected to be used as a fundamental research for developing a smart chair that can induce a correct sitting posture in children by using a sensor-based cushion. [PERSON] contributed to design and conduct the experiments and drafted all sections of the manuscript. [PERSON] contributed to the algorithms design and the revision of the manuscript. [PERSON] supervised the entire research and contributed any improvements until the manuscript was finalized. [PERSON] contributed to conduct the experiment and illustrated figures in manuscript. [PERSON] proposed new discussion points on the research results and provided an idea of how this research could be utilized. All authors contributed to the proofreading of the final manuscript. This research was supported by the BK21 Plus Program (Centre for Sustainable and Innovative Industrial Systems) funded by the Korea government (Ministry of Education) under Grant [No. 21A20130012638]. This study was also supported by the Korea Evaluation Institute of Industrial Technology (KEIT) funded by the Korea government (Ministry of Trade, Industry, and Energy) under Grant [No. 1415152418]. The authors would like to thank Algorigo Inc. for providing the sensor system during this research, and we would like to thank DBK Co., Ltd. for providing the chair for children. We appreciate the administrative support from the Institute for Industrial Systems Innovation of Seoul National University. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: