Video-based Fall Detection for Seniors with Human Pose Estimation

Abstract

In recent years, aging of population and empty nest problem are becoming more and more severe. In addition, fall is the leading cause of death for seniors both in China and the U.S. Therefore, automatic fall detection for seniors is required in smart home and smart healthcare system. Currently, for its convenience and low cost, video-based method is the optimal method compared with other methods such as wearable sensor and ambient sensor in the field of indoor fall detection. In this paper, we propose a novel 2D video-based fall detection pipeline with human pose estimation. Firstly, we used OpenPose to extract the positions of human joints in raw data. Secondly, these data with augmented features became the input of a convolution neural network so that we can extract multi-layered features. Thirdly, a binary classification was conducted through neural network. For comparison, we also used SVM as the classifier. At last, we achieved relatively high sensitivity and specificity when compared our results with other state-of-the-art approaches on three public fall datasets.