MM-FGRM: Fine-Grained Respiratory Monitoring Using MIMO Millimeter Wave Radar

Long-term respiratory monitoring plays an extremely important role in the diagnosis of respiratory system-related diseases. It can detect diseases temporally and improve the effectiveness of treatment, which is crucial for home health monitoring. With the rapid development of radar chip technology, noncontact respiratory monitoring based on commercial millimeter-wave radar sensors has received increasing attention. However, traditional signal processing methods are difficult to extract fine respiratory waveforms from radar signals mixed with human body movements. Fortunately, deep learning provides a solution, as we can use its powerful learning ability to learn a mapping from radar signals to real respiratory waveforms. With this method, we can directly observe fine-grained respiratory waveforms and further improve the accuracy of respiratory rate detection. Therefore, we propose MM-FGRM, a respiratory monitoring system based on a commercial 77 GHz multiple input multiple output (MIMO) radar. The core of this system is a deep learning-based network called IQ-Transformer with a self-attention mechanism that aims at capturing the latent respiratory-related features from I/Q components of radar signals directly in each human body region of interest (ROI) and recovering the respiratory waveforms. We collected 12 h of data from eight subjects and conducted experiments. The experimental results show that MM-FGRM can accurately recover respiratory waveforms and provide accurate respiratory rates. In addition, we perform testing on data from two other users who do not participate in the training, and the results verify that MM-FGRM has a strong generalization ability. Our results demonstrate the feasibility of further development of home respiratory monitoring products.