Fall Risk Estimation for Inpatients on Beds Using 3D Vision Sensor

Fall accidents at medical centers are one of the most serious problems, and many accidents happen at the bedside. In order to deal with this problem, this paper proposes a method for estimating an inpatient's fall risk using only depth data from Kinect. In this method, the obtained depth data are modified as if Kinect were above the center of the bed to estimate fall risks accurately. Then, these corrected data are divided into multiple cells, and human location is detected using gravity coordinates obtained from the data in each cell. Finally, the fall risks are estimated using the human location and predefined fall risk weights. Through verification using hospitalized subjects, the authors confirmed that the proposed method can estimate appropriate fall risks.