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Article

Improved Self-Organizing Map-Based Unsupervised Learning Algorithm for Sitting Posture Recognition System

1
College of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China
2
College of Electrical Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Roozbeh Ghaffari
Sensors 2021, 21(18), 6246; https://doi.org/10.3390/s21186246
Received: 3 August 2021 / Revised: 25 August 2021 / Accepted: 15 September 2021 / Published: 17 September 2021
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. View Full-Text
Keywords: sitting posture recognition; flexible pressure array; self-organizing map; unsupervised self-learning algorithm sitting posture recognition; flexible pressure array; self-organizing map; unsupervised self-learning algorithm
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MDPI and ACS Style

Cai, W.; Zhao, D.; Zhang, M.; Xu, Y.; Li, Z. Improved Self-Organizing Map-Based Unsupervised Learning Algorithm for Sitting Posture Recognition System. Sensors 2021, 21, 6246. https://doi.org/10.3390/s21186246

AMA Style

Cai W, Zhao D, Zhang M, Xu Y, Li Z. Improved Self-Organizing Map-Based Unsupervised Learning Algorithm for Sitting Posture Recognition System. Sensors. 2021; 21(18):6246. https://doi.org/10.3390/s21186246

Chicago/Turabian Style

Cai, Wenyu, Dongyang Zhao, Meiyan Zhang, Yinan Xu, and Zhu Li. 2021. "Improved Self-Organizing Map-Based Unsupervised Learning Algorithm for Sitting Posture Recognition System" Sensors 21, no. 18: 6246. https://doi.org/10.3390/s21186246

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