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Appl. Sci. 2018, 8(8), 1280; https://doi.org/10.3390/app8081280

Classification of Children’s Sitting Postures Using Machine Learning Algorithms

1
Department of Industrial Engineering & Institute for Industrial System Innovation, Seoul National University, Seoul 08826, Korea
2
Department of Industrial and Systems Engineering, Dongguk University—Seoul, Seoul 04620, Korea
*
Author to whom correspondence should be addressed.
Received: 11 July 2018 / Revised: 28 July 2018 / Accepted: 30 July 2018 / Published: 1 August 2018
(This article belongs to the Special Issue Deep Learning and Big Data in Healthcare)
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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. View Full-Text
Keywords: consumer products; classification algorithms; image classification; machine learning algorithm; pattern recognition; sensor systems and applications; sitting posture consumer products; classification algorithms; image classification; machine learning algorithm; pattern recognition; sensor systems and applications; sitting posture
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Kim, Y.M.; Son, Y.; Kim, W.; Jin, B.; Yun, M.H. Classification of Children’s Sitting Postures Using Machine Learning Algorithms. Appl. Sci. 2018, 8, 1280.

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