Human Convergence Technology Group, Korea Institute of Industrial Technology, 143 Hanggaulro, Ansan 426-910, Korea.
Department of Biomedical Science and Engineering (BMSE), Institute of Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Korea.
Sensors (Basel). 2018 Jan 12;18(1):208. doi: 10.3390/s18010208.
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.
坐姿监测系统（SPMS）有助于实时评估坐姿者的姿势，并改善坐姿。迄今为止，SPMS 研究报告需要在椅子的靠背板和座板上安装许多传感器。因此，本研究开发了一种系统，该系统仅在座板上安装了四个负载单元，即可测量包括靠背板受力时的坐姿在内的总共六种坐姿。将开发的 SPMS 测量的体重比应用于各种机器学习算法，以确定最准确地分类坐姿者实际坐姿的方法。使用几种分类器对坐姿进行分类后，使用径向基函数核的支持向量机从 9 名受试者中获得了平均和最大分类率分别为 97.20%和 97.94%；该分类器的结果与使用其他分类器进行的多次分类的结果相比具有统计学意义。所提出的 SPMS 能够分类包括靠背受力的六种坐姿，并且即使减少了传感器的数量，也显示出了分类坐姿的可能性。
