Improved Self-Organizing Map-Based Unsupervised Learning Algorithm for Sitting Posture Recognition System
As the intensity of work increases, many of us sit for long hours while working in the office. It is not easy to sit properly at work all the time and sitting for a long time with wrong postures may cause a series of health problems as time goes by. In addition, monitoring the sitting posture of pat...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-09-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/18/6246 |
| _version_ | 1827681255970308096 |
|---|---|
| author | Wenyu Cai Dongyang Zhao Meiyan Zhang Yinan Xu Zhu Li |
| author_facet | Wenyu Cai Dongyang Zhao Meiyan Zhang Yinan Xu Zhu Li |
| author_sort | Wenyu Cai |
| collection | DOAJ |
| description | As the intensity of work increases, many of us sit for long hours while working in the office. It is not easy to sit properly at work all the time and sitting for a long time with wrong postures may cause a series of health problems as time goes by. In addition, monitoring the sitting posture of patients with spinal disease would be beneficial for their recovery. Accordingly, this paper designs and implements a sitting posture recognition system from a flexible array pressure sensor, which is used to acquire pressure distribution map of sitting hips in a real-time manner. Moreover, an improved self-organizing map-based classification algorithm for six kinds of sitting posture recognition is proposed to identify whether the current sitting posture is appropriate. The extensive experimental results verify that the performance of ISOM-based sitting posture recognition algorithm (ISOM-SPR) in short outperforms that of four kinds of traditional algorithms including decision tree-based (DT), K-means-based (KM), back propagation neural network-based (BP), self-organizing map-based (SOM) sitting posture recognition algorithms. Finally, it is proven that the proposed system based on ISOM-SPR algorithm has good robustness and high accuracy. |
| first_indexed | 2024-03-10T07:13:38Z |
| format | Article |
| id | doaj.art-8f35fe9f87ec4357b2e44ee0905a3a2c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T07:13:38Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8f35fe9f87ec4357b2e44ee0905a3a2c2023-11-22T15:13:54ZengMDPI AGSensors1424-82202021-09-012118624610.3390/s21186246Improved Self-Organizing Map-Based Unsupervised Learning Algorithm for Sitting Posture Recognition SystemWenyu Cai0Dongyang Zhao1Meiyan Zhang2Yinan Xu3Zhu Li4College of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, ChinaCollege of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, ChinaCollege of Electrical Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, ChinaCollege of Electrical Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, ChinaCollege of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, ChinaAs the intensity of work increases, many of us sit for long hours while working in the office. It is not easy to sit properly at work all the time and sitting for a long time with wrong postures may cause a series of health problems as time goes by. In addition, monitoring the sitting posture of patients with spinal disease would be beneficial for their recovery. Accordingly, this paper designs and implements a sitting posture recognition system from a flexible array pressure sensor, which is used to acquire pressure distribution map of sitting hips in a real-time manner. Moreover, an improved self-organizing map-based classification algorithm for six kinds of sitting posture recognition is proposed to identify whether the current sitting posture is appropriate. The extensive experimental results verify that the performance of ISOM-based sitting posture recognition algorithm (ISOM-SPR) in short outperforms that of four kinds of traditional algorithms including decision tree-based (DT), K-means-based (KM), back propagation neural network-based (BP), self-organizing map-based (SOM) sitting posture recognition algorithms. Finally, it is proven that the proposed system based on ISOM-SPR algorithm has good robustness and high accuracy.https://www.mdpi.com/1424-8220/21/18/6246sitting posture recognitionflexible pressure arrayself-organizing mapunsupervised self-learning algorithm |
| spellingShingle | Wenyu Cai Dongyang Zhao Meiyan Zhang Yinan Xu Zhu Li Improved Self-Organizing Map-Based Unsupervised Learning Algorithm for Sitting Posture Recognition System Sensors sitting posture recognition flexible pressure array self-organizing map unsupervised self-learning algorithm |
| title | Improved Self-Organizing Map-Based Unsupervised Learning Algorithm for Sitting Posture Recognition System |
| title_full | Improved Self-Organizing Map-Based Unsupervised Learning Algorithm for Sitting Posture Recognition System |
| title_fullStr | Improved Self-Organizing Map-Based Unsupervised Learning Algorithm for Sitting Posture Recognition System |
| title_full_unstemmed | Improved Self-Organizing Map-Based Unsupervised Learning Algorithm for Sitting Posture Recognition System |
| title_short | Improved Self-Organizing Map-Based Unsupervised Learning Algorithm for Sitting Posture Recognition System |
| title_sort | improved self organizing map based unsupervised learning algorithm for sitting posture recognition system |
| topic | sitting posture recognition flexible pressure array self-organizing map unsupervised self-learning algorithm |
| url | https://www.mdpi.com/1424-8220/21/18/6246 |
| work_keys_str_mv | AT wenyucai improvedselforganizingmapbasedunsupervisedlearningalgorithmforsittingposturerecognitionsystem AT dongyangzhao improvedselforganizingmapbasedunsupervisedlearningalgorithmforsittingposturerecognitionsystem AT meiyanzhang improvedselforganizingmapbasedunsupervisedlearningalgorithmforsittingposturerecognitionsystem AT yinanxu improvedselforganizingmapbasedunsupervisedlearningalgorithmforsittingposturerecognitionsystem AT zhuli improvedselforganizingmapbasedunsupervisedlearningalgorithmforsittingposturerecognitionsystem |