Vision-based Bed Detection for Hospital Patient Monitoring System.

Abstract

In recent years, as a way to prevent patient fall-down, studies have been conducted using patient room cameras to detect the patient behavior of leaving the bed. It is very important to specify the patient bed location in the process of detecting patient behavior using camera images. In this study, we propose a method to specify the patient bed location using a monocular camera. In this proposal, we convert a camera image viewpoint into a bird's-eye view image as a preprocessing step. By using planer perspective transformation, it is possible to display the bed as a rectangular shape with a fixed ratio, even if the bed location or camera position is changed. Therefore, it is possible to detect the bed location with a high degree of accuracy by means of machine learning. The simulation experiment results confirm that the average error and standard deviation of the bed coordinates are 7.9 and 5.0 pixels, respectively; in the practical scene, we confirm that the average error and standard deviation of the bed coordinates are 12.1 and 8.2 pixels, respectively.