Abstract: 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.

Key words: elderly care service, fall detection, posture estimation, classification model, video recognition