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.

中文翻译：

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用于坐姿识别系统的改进的基于自组织地图的无监督学习算法

随着工作强度的增加，我们中的许多人在办公室工作时会长时间坐着。在工作中一直坐好并不容易，久坐久坐不正确的姿势可能会随着时间的推移导致一系列健康问题。此外，监测脊柱疾病患者的坐姿也有利于他们的康复。因此，本文设计并实现了一种基于柔性阵列压力传感器的坐姿识别系统，用于实时获取坐姿臀部的压力分布图。此外，提出了一种改进的基于自组织图的六种坐姿识别分类算法，以识别当前坐姿是否合适。大量的实验结果验证了基于ISOM的坐姿识别算法（ISOM-SPR）的性能在短期内优于基于决策树（DT），基于K-means（KM），back基于传播神经网络（BP）、基于自组织地图（SOM）的坐姿识别算法。最后，证明了所提出的基于ISOM-SPR算法的系统具有良好的鲁棒性和高精度。