# Classification of Children's Sitting Postures Using Machine Learning Algorithms
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**20 kg**} & \multicolumn{2}{c}{**30 kg**} & \multicolumn{2}{c}{**40 kg**} & \multicolumn{2}{c}{**ANOVA**} \\ \cline{2-9}  & **Mean** & **Std.** & **Mean** & **Std.** & **Mean** & **Std.** & **F** & \(p\) \\ \hline Pa & 0.883 & 0.051 & 0.875 & 0.098 & 0.998 & 0.002 & 0.046 & 0.883 \\ Pb & 0.939 & 0.034 & 0.983 & 0.018 & 0.989 & 0.010 & 0.044 & 0.939 \\ Pc & 0.912 & 0.062 & 0.937 & 0.091 & 0.984 & 0.026 & 0.349 & 0.912 \\ Pd & 0.947 & 0.059 & 1.000 & 0.000 & 1.000 & 0.000 & 0.117 & 0.947 \\ Pe & 0.885 & 0.090 & 0.976 & 0.033 & 0.984 & 0.032 & 0.108 & 0.885 \\ Ra & 0.887 & 0.086 & 0.935 & 0.083 & 0.986 & 0.019 & 0.212 & 0.887 \\ Rb & 0.931 & 0.071 & 0.923 & 0.086 & 0.995 & 0.007 & 0.270 & 0.931 \\ Rc & 0.883 & 0.090 & 0.908 & 0.080 & 1.000 & 0.000 & 0.099 & 0.883 \\ Rd & 0.931 & 0.043 & 0.986 & 0.018 & 0.978 & 0.034 & 0.163 & 0.931 \\ Re & 0.912 & 0.067 & 0.985 & 0.027 & 0.993 & 0.008 & 0.060 & 0.912 \\ Accuracy & 0.909 & 0.007 & 0.947 & 0.012 & 0.991 & 0.009 & 0.000 & 0.909 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Result of ANOVA between weight groups for each performance metric. [PERSON] et al. [31] reported 96% accuracy for a familiar user; but 79% accuracy for an unfamiliar user. In addition, in a study by [PERSON] et al. [29], the result of the classification performance for unfamiliar users was more than 90%, but it was approximately 5% lower than that obtained for familiar users. Therefore, the classification of the sitting postures of unfamiliar users is a more challenging approach in the derivation of the classification accuracy. Furthermore, considering the case of the classification of the sitting postures of a new user after constructing the database in advance without utilizing the data of the existing user, an approach for predicting the unfamiliar user is required. Thus, this study adopted a more conservative verification method which identifies unfamiliar users. In addition, in this study, the participants were children and, not adults, who were dominantly focused on in previous studies. The anthropometric characteristics of children are largely distributed as they are in the process of growing. In other words, the variance in the child's anthropometric variables, such as height and weight, is greater than that in adult groups. This encouraged us to conduct further analysis based on the characteristics of the participants, especially their weight. As a result, the classification performance of the low weight group was lower than that of the high weight group. This result is consistent with that obtained in a previous study [51]. The authors of this previous study divided the groups according to the level of body mass index (BMI) and compared the classification performance. The results showed that light intensity group (low BMI) had a lower classification performance than the moderate intensity group (normal BMI). Although this result was obtained using data from adults, it implies the same issue as this current study: when performing experiments on groups of similar age, anthropometric characteristics such as body weight may influence the classification accuracy. Therefore, it is necessary to consider the human body characteristics such as weight in the posture classification. Thinking in the other direction, it is expected that a higher accuracy would be obtained if weight is used as an input variable in the classification of sitting postures. Therefore, it would be necessary to construct a database and train the obtained data by weight groups. The current paper has collected data for 10 children, but more data will be obtained in the future to utilize a larger data size for classifying the sitting posture. Since the posture can be sufficiently classified through the system constructed in this study, it is expected that the reliability of the result can be guaranteed by acquiring more data.