Falls are a very common unexpected accident that result in serious injuries such as broken bones, head injury. Detecting falls, taking fall patients to the emergency room, and sending notification to their family in time is very important. In this paper, we propose a method that combines face recognition and action recognition for fall detection. Specifically, we identify seven basic actions that take place in elderly daily life based on skeleton data extracted using YOLOv7-Pose model. Two deep models which are Spatial Temporal Graph Convolutional Network (ST-GCN), and Long Short-Term Memory (LSTM) are employed for action recognition on the skeleton data. The experimental results on our dataset show that ST-GCN model achieved an accuracy of 90% that is 7% higher than the LSTM model.
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