Application of graph convolutional spatial temporal fusion model based on gated recurrent unit and attention mechanism in inertial signal human activity recognition

The study of human activity recognition systems based on wearable sensors is crucial in medical rehabilitation training, game entertainment, sports analysis and other fields. A new-style hybrid deep learning model based on graph convolutional network, gated recurrent unit and attention mechanism is first proposed to recognize human activity in real-time. In order to demonstrate the superiority of the proposed hybrid model, this paper trains and verifies the proposed model, convolutional neural network model and graph convolutional network model on the open source daily life activity dataset. The experiment results indicate that the accuracy and average recall rate of the proposed model is 97.8% and 97.3% respectively, which higher than convolutional neural network model and graph convolutional network model. In addition, the accuracy of the proposed model also exceeds other state-of-the-art recognition models. Therefore, the proposed novel hybrid model with higher accuracy and stronger robustness is favorable to the practice application of human activity recognition system based on wearable sensors.