Multi-source processing technology of wearable psychological monitoring device based on CNN-LSTM
In recent years,the existing multi-source processing technology of wearable psychological monitoring devices still has problems such as abnormal and unstable data recognition.In order to solve this problem,a multi-source processing technology of wearable psychological monitoring device is proposed,which integrates convolutional neural network and long and short term memory network.Firstly,the research analyzes the multi-source processing technology of wearable psychological monitoring device,and then the performance of the multi-source processing model of wearable psychological monitoring device is analyzed.The experimental re-sults show that with the increase of the number of iterations,when the number of iterations of the training set reaches 4,the loss change curve of the model begins to stabilize,and the loss value is 0.05.At the same time,when the number of iterations of the training set reaches 6,the accuracy curve of the model also begins to stabilize.At this time,the accuracy of the model is as high as 98.82％,which shows that the research model can improve the accuracy and real-time monitoring of mental states.
psychological monitoring devicemulti-source processing technologyCNN-LSTMmental state warning
万方数据
维普
