基于视频和人体姿态估计的老年人摔倒监测研究

人口老龄化所带来的养老服务问题是现代社会面临的严重问题。例如在很多国家跌倒是造成老年人因伤致死的最大原因，因此如何对老年人进行自动摔倒监测就成为养老服务亟待解决的问题。目前，在室内摔倒监测领域中，基于可穿戴设备和基于环境传感器等主流摔倒监测方法面临着设备复杂、成本较高等问题。鉴于此，将人体姿态估计引入摔倒监测领域，提出了一种基于2D视频的摔倒监测算法。首先利用OpenPose数据集提取原始数据中人体关节的位置；其次利用这些具有增强特征的数据构建静态分类模型和动态分类模型；最后，在3个公共摔倒数据集上进行模型训练和摔倒监测的测试，取得了较好的效果，可以为摔倒监测相关研究提供一定的参考。

Fall detection of old people based on video and human posture estimation

The problem of elderly care services brought about by the aging population is a serious problem faced by modern society. For example, in many countries, falls are the biggest cause of death due to injuries among the elderly. Therefore, how to perform automatic fall detection for the elderly has become an urgent problem to be solved in elderly care services. At present, in the field of indoor fall detection, mainstream fall detection methods based on wearable devices and environmental sensors are facing problems such as complex equipment and high cost. In view of this, this paper introduces human body posture estimation into the field of fall detection, and proposes a fall detection method based on two-dimensional video. Firstly, the OpenPose data set is used to extract the positions of human joints in the original data. Secondly, these data with enhanced features are used to build static classification models and dynamic classification models. Finally, model training and fall detection are tested on three public fall data sets, achieving good results. The results of this research can provide a certain reference for the related research of fall detection.