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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| Format: | Article |
| Language: | English |
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MDPI AG
2018-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/18/1/208 |
| _version_ | 1817991495352844288 |
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| author | Jongryun Roh Hyeong-jun Park Kwang Jin Lee Joonho Hyeong Sayup Kim Boreom Lee |
| author_facet | Jongryun Roh Hyeong-jun Park Kwang Jin Lee Joonho Hyeong Sayup Kim Boreom Lee |
| author_sort | Jongryun Roh |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-14T01:14:05Z |
| format | Article |
| id | doaj.art-48d971881f6241fa938ef57b3b75b2a6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T01:14:05Z |
| publishDate | 2018-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-48d971881f6241fa938ef57b3b75b2a62022-12-22T02:20:56ZengMDPI AGSensors1424-82202018-01-0118120810.3390/s18010208s18010208Sitting Posture Monitoring System Based on a Low-Cost Load Cell Using Machine LearningJongryun Roh0Hyeong-jun Park1Kwang Jin Lee2Joonho Hyeong3Sayup Kim4Boreom Lee5Human Convergence Technology Group, Korea Institute of Industrial Technology, 143 Hanggaulro, Ansan 426-910, KoreaDepartment of Biomedical Science and Engineering (BMSE), Institute of Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Gwangju 61005, KoreaDepartment of Biomedical Science and Engineering (BMSE), Institute of Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Gwangju 61005, KoreaHuman Convergence Technology Group, Korea Institute of Industrial Technology, 143 Hanggaulro, Ansan 426-910, KoreaHuman Convergence Technology Group, Korea Institute of Industrial Technology, 143 Hanggaulro, Ansan 426-910, KoreaDepartment of Biomedical Science and Engineering (BMSE), Institute of Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Gwangju 61005, KoreaSitting 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.http://www.mdpi.com/1424-8220/18/1/208sitting posture monitoring systemmachine learningsupport vector machinesitting posture classificationload cell |
| spellingShingle | Jongryun Roh Hyeong-jun Park Kwang Jin Lee Joonho Hyeong Sayup Kim Boreom Lee Sitting Posture Monitoring System Based on a Low-Cost Load Cell Using Machine Learning Sensors sitting posture monitoring system machine learning support vector machine sitting posture classification load cell |
| 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 | sitting posture monitoring system machine learning support vector machine sitting posture classification load cell |
| url | http://www.mdpi.com/1424-8220/18/1/208 |
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