# Classification of Children's Sitting Postures Using Machine Learning Algorithms
## 5 Discussion  
The objective of this study is to classify the sitting postures of children using pressure sensors installed on the seat using the CNN algorithm. To verify the applicability of this algorithm, several learning algorithms including NB, DT, NN, and SVM were applied to the same data set for the classification of the sitting postures. The accuracy of DT was 0.794, which was the lowest among those of the applied models. The performance of NB and MLR was 0.871 and 0.845, respectively. The CNN, NN, and SVM algorithms showed more than 90% classification accuracy, and the CNN model showed the highest classification accuracy. The classification performance of SVM was 0.942, which approximately had a 1% difference as compared to that of the CNN. Besides that, the standard deviation obtained using CNN was smaller than that of SVM (std. = 0.037). It is essential in the context of classification that value of accuracy is high and the standard deviation value is small [50]. Therefore, CNN is noted to have shown the best result through high average accuracy value and small accuracy deviation between the participants. Previous researches can be classified into cases wherein a pressure sensor is used only on the seat and wherein the pressure sensor is attached to both the seat and backrest. According to [PERSON] et al. [9], the accuracy was low when only the pressure data obtained from the seat was utilized without using the pressure data of the backrest. From this result, it can be inferred that under the same experimental conditions, more data can be utilized if the pressure information of the backrest is obtained in addition to that of the seat, thus resulting in a better classification performance. Therefore, it was more challenging to classify the sitting posture by using pressure data only from the seat and not from the backrest. Nonetheless, the average classification accuracy of CNN was 95.3%, which was not significantly lower than those in previous studies using both seat and backrest data [31; 32] (See Table 1). Indeed, the classification performance of this study is better than that presented in several other studies [10; 30]. These two previous studies classified sitting postures using the NB classifier, which was also used in this study. Although the absolute comparison of the classification performance is limited owing to differences in experimental conditions, it can be inferred that the NB algorithm is not suitable for posture classification given that the results of NB are not high in this study (See Table 4). Moreover, it can be observed that the various types of algorithms have been utilized in diverse methods in the area of research of posture classification. This suggests that attempts have been made at using various algorithms to realize higher classification performance rather than focusing on specific algorithms based on optimal experimental setups. Therefore, according to these trends, the application of CNN, which has not been employed in such an application thus far, can be considered as a valuable new approach. The classification performance obtained for familiar users is generally more than 90%; however, there were cases in which the performance of unfamiliar users was less than 80%. For instance,