# Classification of Children's Sitting Postures Using Machine Learning Algorithms
## 1 Introduction  
The sedentary lifestyle is prevalent in modern society. Among the sedentary behaviors, sitting in a chair is a routine behavior of office workers and students in their work environment. However, prolonged sitting behavior and awkward sitting postures have been reported to act as negative factors affecting health. Prolonged sitting has been known to be associated with premature mortality [1], chronic illness [2], metabolic syndrome [3], and obesity [4]. Moreover, sitting with poor posture could have a negative influence on the lumbar health [5]. An excessive flexion and extension of the lumbar are representative patterns of poor sitting postures, which cause high compression in the lumbar [6]. In particular, back pain in childhood caused by improper sitting posture can be a risk factor for lumbar disease in adulthood [7]. Therefore, it is important to form correct postural habits from childhood. To prevent the occurrence of these illnesses, one should maintain a good sitting posture and take a break at the appropriate time intervals when performing tasks. However, it is difficult for people to maintain a correct posture with constant awareness or to change one's incorrect postural habits by oneself. On this basis, researches have been conducted for developing a posture monitoring system that helps people to maintain a correct posture. The dominant approach is to use pressure distribution data obtained from pressure sensors attached to chair structures such as a seat and backrest. To realize this system, it is necessary for the process of classifying the sitting posture accurately in real time to precede the development of the posture monitoring system. Previous studies have adopted several conventional algorithms for classifying sitting postures such as the Hidden Markov Models (HMM), Naive Bayes (NB) classifier and k-nearest neighbor (kNN) classifier [8; 9; 10]. Conventional machine learning algorithms including neural network, support vector machine (SVM), and kNN are still being adopted in various research objectives such as fault diagnosis, wind speed prediction, and thermal anomalies identification [11; 12; 13; 14]. Recently, it has been proven that high performance can be obtained by using deep learning in various research fields such as image processing, and speech recognition. The convolutional neural network (CNN) is a prevalent type of deep learning algorithm, and it has a structure that can fully utilize two-dimensional input data. Thus, it has been widely applied to various image recognition fields such as motor fault diagnosis, vision-based mobile robot navigation, physiological signal analysis in medical assessment and human emotion recognition [15; 16; 17; 18]. To our best knowledge, however, the CNN algorithm has not been used for research on the classification of sitting postures for children. As the pressure distribution data has two-dimensional of image structure, it is reasonable to assume that the sitting posture presented based on the pressure distribution could be classified using a CNN algorithm. Previous studies on the classification of sitting postures were not focused on a particular algorithm. The reason for applying various algorithms is that the classification performance is affected by both the characteristics of the algorithm and the data set. It can be inferred that it is necessary to identify the optimal classification algorithm for various experimental environments. Most of the previous studies have focused on adults only, not children. However, as children have different physical characteristics from adults and as children in the growth phase exhibit large variations within a group, the results of previous experiments may differ from those obtained in studies comprising adult participants [19]. Furthermore, in studies that perform postural classification, it is important to target children since the habit of sitting in the right posture in childhood is more important when compared with adulthood.