An Approach to Real-Time Fall Detection based on OpenPose and LSTM

Abstract

Falls are consistently the top cause of death among seniors. At a time when the global population is getting older and fewer births. The shortage of nursing staff seriously affects the health care of the elderly. If information and communication technology can be used, automatic detection and identification the elderly fall, we believe it can reduce the injury of the elderly due to falls. This paper proposes a method different from the previous wearable sensing device, which is based on the displacement of human relative positional parameters in the image to identify the occurrence of human fall. We implemented a system based on OpenPose and combined with the deep learning neural network model LSTM with time series, the image recognition is carried out, the human joint parameters of human posture falling and falling in the image are captured, and the identified parameters are simply filtered, and then the filtered parameters are used for model training.