基于姿态估计的实时跌倒检测算法

为了快速、准确地检测老年人跌倒事件的发生,给出一种基于姿态估计的实时跌倒检测算法.首先利用基于深度学习的人体姿态估计算法得到人体关节点的坐标;然后,通过计算人体质心点的下降速度、跌倒后颈部关节点的纵坐标值是否大于阈值、以及肩部和腰部关节点的相对位置关系来判断跌倒是否发生.所给出的跌倒检测算法利用单目相机进行检测,便于以嵌入式方式应用于机器人.实验结果表明,所提出算法与当前先进方法相比取得了较好的效果.

Real-time fall detection algorithm based on pose estimation

In order to quickly and accurately detect the occurrence of falls in the elderly, this paper presents a real-time fall detection algorithm based on pose estimation. Firstly, the human pose estimation algorithm based on deep learning is used to obtain the coordinates of the joint point. Then, by calculating the falling speed of the centroid point when the human body falls, whether the ordinate value of the neck joint point after the fall is greater than the threshold, and the relative positional relationship of the shoulder-waist joint point in the image, whether the fall occurs is determined. The algorithm uses a monocular camera to detect, which is easily used in an embedded way for robots. The experimental results show that compared with the current advanced methods, the proposed algorithm has achieved good results.