CORA: Continuous Respiration Monitoring Using Analytical Signal Processing

Acoustic-based respiration sensing is promising due to its ubiquitous device support and great freedom in signal design. However, existing proposals often either fail to function properly when a target is non-static or is under multipath interference, or address it in an algorithmic manner. To this end, in this paper, we propose CORA, a COntinuous RespirAtion monitoring system using purely analytical signal processing methods. CORA is the first approach that achieves physical separation between motion artifacts and respiration, other than existing algorithmic solutions, and hence can obtain results that are closer to ground truth. CORA leverages the edges of Orthogonal Time Frequency Space signals in monitoring motion states and addressing multipath interference. The ability to tackle these challenges can help to compensate motion-induced artifacts for FMCW-based sensing techniques, enabling continuous respiration monitoring even in non-static scenarios. To achieve high-quality compensation, a pipeline of signal processing techniques is proposed, including robust moving target tracking, accurate frequency bin selection, and effective phase denoising. Unlike existing deep learning-based approaches, CORA is explainable and is readily deployable, without sophisticated adaptation or exhausted training processes. We have implemented a system prototype and evaluated its performance. Experiment results demonstrate a median error of 0.86 respiration per minute.