Fall detection algorithm for the elderly based on human posture estimation

With the acceleration of the global aging process, the safety and health of the elderly has become a widespread concern, and falls have become the main health threat of the elderly. In this paper, a fall detection model based on OpenPose human posture estimation algorithm is proposed by using the fall detection method based on machine vision. On the basis of OpenPose human key point detection, combined with SSD MobileNet object detection framework can remove the non human key points detected by OpenPose algorithm, reduce the false detection rate of the algorithm, improve the robustness of the algorithm in complex environment, and then extract the features of human joint points, use SVDD classification algorithm to classify, experiments show that this method can effectively detect the occurrence of falls, and the accuracy rate can reach more than 93%.