Sitting posture recognition based on human body pressure and CNN

Abstract

With the development of society, the proportion of sitting time in people’s daily life and working environment is increasing. However, Most of the sitting posture is improper sitting posture in most of the time. Improper sitting posture for a long time is one of the main causes of a series of skeletal muscle diseases. In this paper, a CNN sitting posture recognition model based on human pressure data is proposed. The model is constructed by collecting a large number of pressure data of human-chair contact surface and using these training to get sitting posture recognition algorithm. Experiments show that the algorithm can accurately identify eight kinds of human sitting posture. Its accuracy rate is as high as 95.6%, and the recall rate is 95.5% at same time. The sitting position recognition system constructed by this algorithm can monitor and distinguish the bad sitting posture of human body in real time, and it has the many advantages such as high accuracy, high robustness, high availability and high security.