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Sensors 2018, 18(1), 208; doi:10.3390/s18010208

Sitting Posture Monitoring System Based on a Low-Cost Load Cell Using Machine Learning

Human Convergence Technology Group, Korea Institute of Industrial Technology, 143 Hanggaulro, Ansan 426-910, Korea
Department of Biomedical Science and Engineering (BMSE), Institute of Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Korea
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Received: 23 October 2017 / Revised: 23 December 2017 / Accepted: 11 January 2018 / Published: 12 January 2018
(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)
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Sitting posture monitoring systems (SPMSs) help assess the posture of a seated person in real-time and improve sitting posture. To date, SPMS studies reported have required many sensors mounted on the backrest plate and seat plate of a chair. The present study, therefore, developed a system that measures a total of six sitting postures including the posture that applied a load to the backrest plate, with four load cells mounted only on the seat plate. Various machine learning algorithms were applied to the body weight ratio measured by the developed SPMS to identify the method that most accurately classified the actual sitting posture of the seated person. After classifying the sitting postures using several classifiers, average and maximum classification rates of 97.20% and 97.94%, respectively, were obtained from nine subjects with a support vector machine using the radial basis function kernel; the results obtained by this classifier showed a statistically significant difference from the results of multiple classifications using other classifiers. The proposed SPMS was able to classify six sitting postures including the posture with loading on the backrest and showed the possibility of classifying the sitting posture even though the number of sensors is reduced. View Full-Text
Keywords: sitting posture monitoring system; machine learning; support vector machine; sitting posture classification; load cell sitting posture monitoring system; machine learning; support vector machine; sitting posture classification; load cell

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Roh, J.; Park, H.-J.; Lee, K.J.; Hyeong, J.; Kim, S.; Lee, B. Sitting Posture Monitoring System Based on a Low-Cost Load Cell Using Machine Learning. Sensors 2018, 18, 208.

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