期刊
SENSORS
卷 21, 期 18, 页码 -出版社
MDPI
DOI: 10.3390/s21186246
关键词
sitting posture recognition; flexible pressure array; self-organizing map; unsupervised self-learning algorithm
资金
- National Natural Science Foundation of China [61801431]
- Fundamental Research Funds for the Provincial Universities of Zhejiang
- Zhejiang new seedling plan project [2021R423005, 2021R407059]
- National College Students' innovation and Entrepreneurship Project [202011481014]
The paper presents a sitting posture recognition system using a pressure sensor array to monitor and classify correct sitting postures, with experiments showing its superiority over traditional algorithms.
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.
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