Fall Detection in Elderly Care System Based on Group of Pictures

Abstract

Fall detection is a serious problem in elder people. Constant inspection is important for this fall identification. Currently, numerous methods associated with fall detection are a significant area of research for safety purposes and for the healthcare industries. The objective of this paper is to identify elderly falls. The proposed method introduces keyframe based fall detection in elderly care system. Experiments were conducted on University of Rzeszow (UR) Fall Detection dataset, Fall Detection Dataset and MultiCam dataset. It is substantially proved that the proposed method achieves higher accuracy rate of 99%, 98.15% and 99% for UR Fall detection dataset, Fall Detection Dataset and MultiCam dataset, respectively. The performance of the proposed method is compared with other methods and proved to have higher accuracy rate than those methods.

Keywords

• optical flow
• group of pictures
• foreground segmentation