Interference Motion Removal for Doppler Radar Vital Sign Detection Using Variational Encoder-Decoder Neural Network
This addresses the challenge of accurate vital sign monitoring in radar systems, particularly for healthcare applications, but it is incremental as it builds on existing deep learning methods for signal processing.
The paper tackled the problem of removing interfering motion contributions in Doppler radar vital sign detection by introducing a variational encoder-decoder neural network, which enhanced the extraction of respiration rate micro-Doppler frequencies from semi-experimental data.
The treatment of interfering motion contributions remains one of the key challenges in the domain of radar-based vital sign monitoring. Removal of the interference to extract the vital sign contributions is demanding due to overlapping Doppler bands, the complex structure of the interference motions and significant variations in the power levels of their contributions. A novel approach to the removal of interference through the use of a probabilistic deep learning model is presented. Results show that a convolutional encoder-decoder neural network with a variational objective is capable of learning a meaningful representation space of vital sign Doppler-time distribution facilitating their extraction from a mixture signal. The approach is tested on semi-experimental data containing real vital sign signatures and simulated returns from interfering body motions. The application of the proposed network enhances the extraction of the micro-Doppler frequency corresponding to the respiration rate is demonstrated.