Heart rate variability (HRV) indicates health condition and mental stress. The development of non-contact heart rate (HR) monitoring technique with Doppler radar is attracting great attentions. However, the performance of heartbeat detection via radar signal easily degrades, due to respiration and body motion. In this paper, first, a stochastic gradient approach is applied to reconstruct high-resolution spectrum of heartbeat, by proposing the zero-attracting sign least-mean-square (ZA-SLMS) algorithm. To correct the quantized gradient of cost function, and penalize the sparse constraint on the updating spectrum, more accurate heartbeat spectrum is reconstructed. Then, to better adapt to the noises with different strengths caused by subjects' movements, an adaptive regularization parameter (AREPA) is introduced in the ZA-SLMS algorithm as an improved variant, which can adaptively regulate the proportion between gradient correction and sparse penalty. Moreover, in view of the stability of location of spectral peak associated with HR when the size of time window slightly changes, a time-window-variation (TWV) technique is further incorporated in the improved ZA-SLMS (IZA-SLMS) algorithm, for more stable HR estimation. Through the experiments on five subjects, our proposal is demonstrated to bring a significant improvement of accuracy against existing detection methods. Specifically, the IZA-SLMS algorithm with TWV achieves the smallest average error of 3.79 beats per minute (BPM), when subjects type with a laptop.
ASJC Scopus subject areas
- Biomedical Engineering