Interference motion removal for Doppler radar vital sign detection using variational encoder-decoder neural network

A novel approach to the removal of interference motions through the use of a variational encoder-decoder convolutional neural network is presented for Doppler radar vital sign detection. The approach is evaluated on semi-experimental data containing real vital sign signatures and simulated returns from interfering body motions. It is further demonstrated that the model can enhance the extraction of the micro-Doppler frequency corresponding to the respiration rate.