Bed Exit Action Detection Based on Patient Posture with Long Short-Term Memory
Madoka Inoue, Ryo Taguchi · 2020
It has been known that the fall of a patient in a hospital is a serious accident. In order to prevent such accidents, we have been studying the fall prevention using image processing technology. Our previous studies have detected the patient's end sitting position with high accuracy, but have problems responding to the sitting position of patients who are eating or responding to visitors. In order to solve these problems, this paper proposes a method to detect the patient's bed exit action by analyzing the posture of the patient extracted from the image of the monocular camera by long short-term memory (LSTM). Our proposed method introduces two strategies - abstraction of input information and use of relative position information for the input time-series human images, achieving a 99.2[%] detection rate of bed exit action with a 5.7[%] false detection rate. Detecting the bed exit action with high accuracy contributes to preventing the patient from falling down. The proposed solution handles only posture information that abstracts camera images for patient privacy purposes.