Improvement of a depth camera installation for fall risk estimation

Abstract

We propose a monitoring system for accidental falling of patients using a point cloud dataset (PCD) of the depth camera-captured images. The conventional system requires the PCD to comprise images showing the bed top view. Consequently, the depth camera installation location is restricted. Therefore, we propose a new system with a new PCD generation method. This system enabled PCD correction, corrected dataset division, human location estimation, and fall risk calculation. The Microsoft Kinect sensor was employed as a depth camera in the validation. We demonstrate that the pitch angle can be set between $-20 $ and $35^{\circ } $ within the depth camera measurable range to image the subject's movements. These images were utilized in risk estimation. Further, the horizontal distance from the side edge of the bed and the height from the ground were greater than 1.0 m. Under these conditions, the PCD can be corrected into a bed top view dataset, and can help in estimating the fall risk.

Keywords

• fall risk estimation
• point cloud dataset
• installation constraint
• depth camera
• robustness