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MIMO Radar for Improving Non-Contact Vital-Sign Detection Accuracy
Non-contact Respiration Rate (RR) and Heart Rate (HR) monitoring using mm-wave radars has gained lots of attention for medical, civilian, and military applications. These mm-wave radars are small, light, and portable which can be deployed to various places. To increase the accuracy of RR and HR detection, distributed multi-input multi-output (MIMO) radar can be used to acquire non-redundant information of vital-sign signals from different perspectives because each MIMO channel has different fields of view with respect to the subject under test (SUT). This talk presents the use of a Frequency Modulated Continuous Wave (FMCW) radar operating at 77–81GHz for this application. Continuous Wavelet Transform (CWT) to reduce interferences from respiratory harmonics and RBM, as well as magnify the heartbeat signals. As the nature of RBM is unpredictable, the extracted HBT may not completely cancel the interferences from RBM. Therefore, to provide better HR detection accuracy, we have also developed a spectral-based HR selection method to gather frequency spectra of heartbeat signals from different MIMO channels. Based on this gathered spectral information, we can determine an accurate HR even if the heartbeat signals are significantly concealed by the RBM. To further improve the detection accuracy of RR and HR, two Deep Learning (DL) frameworks were investigated. First, a Convolutional Neural Network (CNN) was been proposed to optimally select clean MIMO channels and eliminate MIMO channels with low SNR of heartbeat signals. After that, a Multi-Layer Perceptron (MLP) Neural Network (NN) was utilized to reconstruct the heartbeat signals that will be used to assess and select the final HR with high confidence.