Key result
The proposed target state detection algorithm improved the accuracy of FMCW radar-based cardiopulmonary monitoring, reducing the mean absolute error for heart rate estimation from 4.7 BPM to 1.9 BPM.
Why the study?
In highly cluttered environments or during random body movement, noise can impede accurate selection of the radar range bin containing target cardiopulmonary signals.
A novel mixed-modal information threshold algorithm for FMCW radar improves the accuracy of non-contact cardiopulmonary signal extraction.
May advance non-contact cardiopulmonary monitoring; leaves open prospective clinical validation.
Frequency-modulated continuous wave radar is capable of constant, real-time detection of human presence and monitoring of cardiopulmonary signals such as respiration and heartbeat. In highly cluttered environments or when the human body moves randomly, noise signals may be relatively large in some range bins, making it crucial to accurately select the range bin containing the target cardiopulmonary signal. In this paper, we propose a target range bin selection algorithm based on a mixed-modal information threshold. We introduce a confidence value in the frequency domain to determine the state of the human target and employ the range bin variance in the time domain to determine the range bin change status of the target. The proposed method accurately detects the state of the target and effectively selects the range bin containing the cardiopulmonary signal with a high signal-to-noise ratio. Experimental results demonstrate that the proposed method achieves better accuracy in cardiopulmonary signal rate estimation. Moreover, the proposed algorithm is lightweight in data processing and has good real-time performance.
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Zhang et al. (2023) studied Healthy (normal vital rates) (n=20). Target state detection algorithm based on mixed-modal threshold vs. Traditional range bin selection method (maximum amplitude) was evaluated on Mean absolute error (MAE) of heart rate estimation (BPM). The proposed target state detection algorithm improved the accuracy of FMCW radar-based cardiopulmonary monitoring, reducing the mean absolute error for heart rate estimation from 4.7 BPM to 1.9 BPM.
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