Human Fall Down Recognition using Coordinates Key Points Skeleton

Falls are a significant hazard to human safety and may have a devastating consequences in a matter of seconds. This is especially true for elderly, since falls are the primary cause of hospitalization and injury-related mortality in this age group. In this paper, a deep learning based model is proposed for analyzing video detected pictures in real-time monitoring camera. Two conditions were tested in order to determine the falling severity. This paper proposes a vision-based method for falling detection. It analyzes an extracted skeleton to identify human posture. Open Pose network is utilized to obtain skeleton information regarding the human body. The falling can be identified using two parameters: the first is the calculation of the angle formed between the shoulder, hip or any of other points (knee, ankle or the heel), the second parameter is the distance calculated during the horizontal falling between the shoulder, hip or any other points such as knee, ankle, heal or the toe. The test results approved that the proposed method outperformed the other state-of-the-art methods in terms of, sensitivity (99.38%), specificity (96%), and accuracy (98 %).