Hip Positioning and Sitting Posture Recognition Based on Human Sitting Pressure Image

Bad sitting posture is harmful to human health. Intelligent sitting posture recognition algorithm can remind people to correct their sitting posture. In this paper, a sitting pressure image acquisition system was designed. With the system, we innovatively proposed a hip positioning algorithm based on hip templates. The average deviation of the algorithm for hip positioning is 1.306 pixels (the equivalent distance is 1.50 cm), and the proportion of the maximum positioning deviation less than three pixels is 94.1%. Statistics show that the algorithm works relatively well for different subjects. At the same time, the algorithm can not only effectively locate the hip position with a small rotation angle (0 degrees-15 degrees), but also has certain adaptability to the sitting posture with a medium rotation angle (15 degrees-30 degrees) or a large rotation angle (30 degrees-45 degrees). Using the hip positioning algorithm, the regional pressure values of the left hip, right hip and caudal vertebrae are effectively extracted as the features, and support vector machine (SVM) with polynomial kernel is used to classify the four types of sitting postures, with a classification accuracy of up to 89.6%.

Automated summary

This paper introduces a sitting posture recognition algorithm based on hip templates which shows good adaptability to different rotation angles, effective feature extraction for classifying four types of sitting postures with a classification accuracy of up to 89.6%.