IEEE J Biomed Health Inform. 2015 Mar;19(2):430-9. doi: 10.1109/JBHI.2014.2319372. Epub 2014 Apr 23.
The elderly population is increasing rapidly all over the world. One major risk for elderly people is fall accidents, especially for those living alone. In this paper, we propose a robust fall detection approach by analyzing the tracked key joints of the human body using a single depth camera. Compared to the rivals that rely on the RGB inputs, the proposed scheme is independent of illumination of the lights and can work even in a dark room. In our scheme, a pose-invariant randomized decision tree algorithm is proposed for the key joint extraction, which requires low computational cost during the training and test. Then, the support vector machine classifier is employed to determine whether a fall motion occurs, whose input is the 3-D trajectory of the head joint. The experimental results demonstrate that the proposed fall detection method is more accurate and robust compared with the state-of-the-art methods.
全球老年人口迅速增加。老年人的一个主要风险是跌倒事故，特别是对于那些独居的人。在本文中，我们提出了一种通过使用单个深度摄像机分析人体跟踪的关键关节来进行稳健跌倒检测的方法。与依赖 RGB 输入的竞争对手相比，该方案不受光照条件的影响，甚至可以在黑暗的房间中工作。在我们的方案中，提出了一种姿态不变随机决策树算法用于关键关节提取，该算法在训练和测试过程中需要较低的计算成本。然后，使用支持向量机分类器来确定是否发生跌倒运动，其输入是头部关节的 3D 轨迹。实验结果表明，与最先进的方法相比，所提出的跌倒检测方法更加准确和稳健。
