论文标题
两阶段秋季事件与人类骨架数据的分类
Two-stage Fall Events Classification with Human Skeleton Data
论文作者
论文摘要
秋季检测和分类成为医疗保健应用特定性的不良问题，因为人口越来越老化。当前，大多数秋季临床化算法都提供二进制跌落或不下垂的分类。为了获得更好的医疗保健，因此进行二元秋季分类还不足以将其扩展到多个秋季事件分类。在这项工作中，我们利用缓解人类骨架数据的隐私性进行多个秋季事件分类。从原始的RGB图像中提取了骨骼功能，不仅可以减轻个人隐私，还可以减少动态照明的影响。提出的秋季事件分类方法分为两个阶段。在第一阶段，训练模型以实现二进制分类以滤除无腹部事件。然后，在第二阶段，对深神经网络（DNN）模型进行了训练，以进一步对五种类型的秋季事件进行分类。为了确认所提出的方法的效率，上下数据集上的实验优于最先进的实验。
Fall detection and classification become an imper- ative problem for healthcare applications particularity with the increasingly ageing population. Currently, most of the fall clas- sification algorithms provide binary fall or no-fall classification. For better healthcare, it is thus not enough to do binary fall classification but to extend it to multiple fall events classification. In this work, we utilize the privacy mitigating human skeleton data for multiple fall events classification. The skeleton features are extracted from the original RGB images to not only mitigate the personal privacy, but also to reduce the impact of the dynamic illuminations. The proposed fall events classification method is divided into two stages. In the first stage, the model is trained to achieve the binary classification to filter out the no-fall events. Then, in the second stage, the deep neural network (DNN) model is trained to further classify the five types of fall events. In order to confirm the efficiency of the proposed method, the experiments on the UP-Fall dataset outperform the state-of-the-art.
