Human activity identification based on CNN-BiLSTM-SA network
In view of the problem that the traditional neural network is not accurate in recognizing human activities,this paper proposes a hybrid network model based on the two-channel mechanism of convolu-tional neural network superimposed with bi-directional long short-term memory network and self-atten-tion(CNN-BiLSTM-SA).First,the acceleration and angular velocity data in the data set are used as the two inputs of the network,and then the system is built by using the convolution neural network to over-lay the bidirectional short-term and short-term memory network;finally,the self-attention mechanism is introduced to enhance the classification ability of the system.The experimental results show that in the UCI-HAR data set,the average F1 score of this network is 98.6％,and that the average accuracy is 98.4％,which is faster than the convolutional neural network-long short-term memory(CNN-LSTM)convergence speed with the accuracy increased by 4.2％,and having a broader application prospect in human activity recognition.
human activity recognitionsensorCNN-BiLSTMself attention
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