Measurement of heart rate variability using off-the-shelf smart phones

Ren-You Huang, Lan-Rong Dung, Ren-You Huang, Lan-Rong Dung

Abstract

Background: The cardiac parameters, such as heart rate (HR) and heart rate variability (HRV), are very important physiological data for daily healthcare. Recently, the camera-based photoplethysmography techniques have been proposed for HR measurement. These techniques allow us to estimate the HR contactlessly with low-cost camera. However, the previous works showed limit success for estimating HRV because the R-R intervals, the primary data for HRV calculation, are sensitive to noise and artifacts.

Methods: This paper proposed a non-contact method to extract the blood volume pulse signal using a chrominance-based method followed by a proposed CWT-based denoising technique. The R-R intervals can then be obtained by finding the peaks in the denoised signal. In this paper, we taped 12 video clips using the frontal camera of a smart phone with different scenarios to make comparisons among our method and the other alternatives using the absolute errors between the estimated HRV metrics and the ones obtained by an ECG-accurate chest band.

Results: As shown in experiments, our algorithm can greatly reduce absolute errors of HRV metrics comparing with the related works using RGB color signals. The mean of absolute errors of HRV metrics from our method is only 3.53 ms for the static-subject video clips.

Conclusions: The proposed camera-based method is able to produce reliable HRV metrics which are close to the ones measured by contact devices under different conditions. Thus, our method can be used for remote health monitoring in a convenient and comfortable way.

Figures

Fig. 1
Fig. 1
The processing flow of the proposed algorithm
Fig. 2
Fig. 2
Examples for using CWT to detrend and denoise. a The original C-rPPG signal. b The CWT coefficients of the original signal. Note that the black solid line denotes the representative frequency (scales) of pulse signal computed by (13). c The zoomed-in part of original signal. d The zoomed-in part of signal denoised by CWT-BP. e The zoomed-in part of signal denoised by CWT-MAX
Fig. 3
Fig. 3
R–R intervals of the example in Fig. 2. The red dot line is the R–R intervals measured by an ECG-accurate chest band. The blue dot line is the R–R intervals computed by our method
Fig. 4
Fig. 4
The illumination changes of the face in the “motion_O3” clip
Fig. 5
Fig. 5
The face positions and illumination in “motion_F1” clip. a The face position (x-axis). b The face position (y-axis). c The illumination (grayscale) of the face

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Source: PubMed

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