Sitting in a chair is a typical act of modern people. Prolonged sitting and sitting with improper postures can lead to musculoskeletal disorders. Thus, there is a need for a sitting posture classification monitoring system that can predict a sitting posture. The purpose of this paper is to develop a system for classifying children’s sitting postures for the formation of correct postural habits.
For the data analysis, a pressure sensor of film type was installed on the seat of the chair, and image data of the postu.re were collected. A total of 26 children participated in the experiment and collected image data for a total of seven postures. The authors used convolutional neural networks (CNN) algorithm consisting of seven layers. In addition, to compare the accuracy of classification, artificial neural networks (ANN) technique, one of the machine learning techniques, was used.
The CNN algorithm was used for the sitting position classification and the average accuracy obtained by tenfold cross validation was 97.5 percent. The authors confirmed that classification accuracy through CNN algorithm is superior to conventional machine learning algorithms such as ANN and DNN. Through this study, we confirmed the applicability of the CNN-based algorithm that can be applied to the smart chair to support the correct posture in children.
This study successfully performed the posture classification of children using CNN technique, which has not been used in related studies. In addition, by focusing on children, we have expanded the scope of the related research area and expected to contribute to the early postural habits of children.
Conflicts of interest: the authors declare no conflicts of interest. The authors appreciate the administrative support from the Institute for Industrial Systems Innovation of Seoul National University. This research was funded by the BK21 Plus Program (Centre for Sustainable and Innovative Industrial Systems) funded by the Ministry of Education, South Korea (No. 21A20130012638). In addition, this research is supported by the Ministry of Culture, Sports and Tourism (MCST) and Korea Culture & Tourism Institute (KCTI) Research & Development Program 2018 (SF0718205).
Kim, W., Jin, B., Choo, S., Nam, C. and Yun, M. (2019), "Designing of smart chair for monitoring of sitting posture using convolutional neural networks", Data Technologies and Applications, Vol. 53 No. 2, pp. 142-155. https://doi.org/10.1108/DTA-03-2018-0021Download as .RIS
Emerald Publishing Limited
Copyright © 2019, Emerald Publishing Limited