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

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 mea...

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Autores principales: Roh, Jongryun, Park, Hyeong-jun, Lee, Kwang Jin, Hyeong, Joonho, Kim, Sayup, Lee, Boreom
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5796304/
https://www.ncbi.nlm.nih.gov/pubmed/29329261
http://dx.doi.org/10.3390/s18010208
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author Roh, Jongryun
Park, Hyeong-jun
Lee, Kwang Jin
Hyeong, Joonho
Kim, Sayup
Lee, Boreom
author_facet Roh, Jongryun
Park, Hyeong-jun
Lee, Kwang Jin
Hyeong, Joonho
Kim, Sayup
Lee, Boreom
author_sort Roh, Jongryun
collection PubMed
description 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.
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spelling pubmed-57963042018-02-13 Sitting Posture Monitoring System Based on a Low-Cost Load Cell Using Machine Learning Roh, Jongryun Park, Hyeong-jun Lee, Kwang Jin Hyeong, Joonho Kim, Sayup Lee, Boreom Sensors (Basel) Article 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. MDPI 2018-01-12 /pmc/articles/PMC5796304/ /pubmed/29329261 http://dx.doi.org/10.3390/s18010208 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Roh, Jongryun
Park, Hyeong-jun
Lee, Kwang Jin
Hyeong, Joonho
Kim, Sayup
Lee, Boreom
Sitting Posture Monitoring System Based on a Low-Cost Load Cell Using Machine Learning
title Sitting Posture Monitoring System Based on a Low-Cost Load Cell Using Machine Learning
title_full Sitting Posture Monitoring System Based on a Low-Cost Load Cell Using Machine Learning
title_fullStr Sitting Posture Monitoring System Based on a Low-Cost Load Cell Using Machine Learning
title_full_unstemmed Sitting Posture Monitoring System Based on a Low-Cost Load Cell Using Machine Learning
title_short Sitting Posture Monitoring System Based on a Low-Cost Load Cell Using Machine Learning
title_sort sitting posture monitoring system based on a low-cost load cell using machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5796304/
https://www.ncbi.nlm.nih.gov/pubmed/29329261
http://dx.doi.org/10.3390/s18010208
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