Research on Human Behavior Recognition based on Deep Neural Network

In order to improve the recognition rate of human behavior by intelligent terminals, a network model for deep learning of human behavior recognition is proposed. Time series data is transformed into a deep network model by performing motion segmentation using a sliding window algorithm. Feature vectors are imported into the SoftMax classifier through end-to-end research, which identifies six daily behaviors such as walking, sitting, going upstairs, going downstairs, standing and lying down. By comparing the recognition effects of different models, it was found that the convolutional neural network introduced into Dropout achieved better recognition results in UCI HAR dataset.