Vital Sign Signal Prediction Algorithm Based on FMCW Radar
The human vital sign signal detected by FMCW radar can be used to predict whether the human vital sign signal is abnormal in the future period of time,which has obvious application value.The current research in this di-rection is mainly aimed at how to further reduce the reconstruction error and improve the prediction accuracy of vital sign signal.In this paper,an adaptive variational mode decomposition long short-term memory(LSTM)neural network is proposed to predict vital sign signal.For the human body in a static state,through the vital sign signal collected by radar,the particle swarm optimization algorithm is used to optimize the value of the number of modal components K and penal-ty coefficient α of the variational mode decomposition VMD,to achieve adaptive selection for VMD decomposition,and then the decomposed modal components are superimposed and reconstructed.The particle swarm optimization algorithm is used to optimize the three parameters of the long short-term memory network model,including the number of network layers,learning rate and regularization coefficient.The appropriate parameter combination is selected adaptively,and the reconstructed signal is predicted through the optimized LSTM network.The experimental results show that the mean square error between the prediction results of 10 volunteers and the original data is 0.017 188 9,and the mean absolute error is 0.007 158.Compared with other current studies,the prediction accuracy is significantly improved.
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