Classification of Children’s Sitting Postures Using Machine Learning Algorithms

Abstract

Sitting on a chair in an awkward posture or sitting for a long period of time is a risk factor for musculoskeletal disorders. A postural habit that has been formed cannot be changed easily. It is important to form a proper postural habit from childhood as the lumbar disease during childhood caused by their improper posture is most likely to recur. Thus, there is a need for a monitoring system that classifies children's sitting postures. The purpose of this paper is to develop a system for classifying sitting postures for children using machine learning algorithms. The convolutional neural network (CNN) algorithm was used in addition to the conventional algorithms: Naïve Bayes classifier (NB), decision tree (DT), neural network (NN), multinomial logistic regression (MLR), and support vector machine (SVM). To collect data for classifying sitting postures, a sensing cushion was developed by mounting a pressure sensor mat (8 × 8) inside children's chair seat cushion. Ten children participated, and sensor data was collected by taking a static posture for the five prescribed postures. The accuracy of CNN was found to be the highest as compared with those of the other algorithms. It is expected that the comprehensive posture monitoring system would be established through future research on enhancing the classification algorithm and providing an effective feedback system.

Improved Self-Organizing Map-Based Unsupervised Learning Algorithm for Sitting Posture Recognition System

“…In the test phase, we compare five traditional methods for sitting posture recognition, namely Decision tree-based SPR(DT-SPR) [ 15 ], K -means based SPR (KM-SPR) [ 29 ], BP neural network based SPR(BPNN-SPR) [ 30 ], SOM network based SPR(SOM-SPR), and the improved SOM network based SPR(ISOM-SPR). Table 3 shows the precision comparable results of different SPR algorithms.…”

“…However, it is difficult to ensure robustness depending on the light conditions and shooting angles [ 14 ], and it also involves some privacy issues. The simple pressure distribution-based approach is to place sensors at some specific locations of a chair or backrest and use feature information collected by these sensors to recognize sitting postures [ 15 ]. This method may be uncomfortable and low accurate.…”

“…Liu et al [ 19 ] apply the convolutional neural network (CNN) to the sitting posture recognition and can achieve 95.5% accuracy. Kim et al [ 15 ] also apply CNN to the sitting posture recognition and compare it with decision tree (DT), multinomial logistic regression (MLR), etc. The average accuracy is 95.3%.…”

Missing Value Imputation in Stature Estimation by Learning Algorithms Using Anthropometric Data: A Comparative Study

“…Recently, machine learning algorithms have been widely used in areas including business and finance, health care, and production due to their superior performance in sophisticated tasks [36][37][38][39][40]. We employed four widely used machine learning classifiers to predict the stature.…”

Hip Positioning and Sitting Posture Recognition Based on Human Sitting Pressure Image

“…At present, there are three main ways of sitting posture recognition, which are based on machine vision [ 2 , 3 , 4 , 5 ], wearable motion sensors [ 6 , 7 , 8 , 9 ] and external pressure sensors [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. Although machine vision technology has achieved great success in the field of posture recognition [ 26 ], it is difficult to work normally in situations with many obstacles.…”