Key result
An adaptively tuned rPPG filter estimated heart rate and HRV from smartphone video with mean absolute errors of 1.13 ± 0.68 bpm and 18.30 ± 10.33 ms, respectively.
Why the study?
To improve the estimation of heart rate and heart rate variability from smartphone video using an adaptively tuned rPPG filter and artifact removal algorithm.
Does an HR-RR-tuned rPPG filter improve the accuracy of heart rate and heart rate variability estimation from smartphone videos?
Does an HR-RR-tuned rPPG filter improve the accuracy of heart rate and heart rate variability estimation from smartphone videos?
An HR-RR-tuned rPPG filter can accurately estimate heart rate and heart rate variability from smartphone videos.
This paper presents algorithms to improve the estimation of heart rate (HR) and heart rate variability (HRV) from smartphone video. The remote photoplethysmogram (rPPG) signals are first extracted from the videos recorded. Next, we proposed an rPPG filter adaptively tuned by HR and respiratory rate (RR) to better enhance source signal that modulates HR. Additionally, we also addressed a unique smartphone artifact—occasionally seen in smartphone videos—by introducing a threshold-based algorithm. HR and HRV accuracies are assessed on 22 subjects who were instructed to breath at seven different RRs. The mean absolute errors of HR and standard deviation of the NN intervals (SDNN) are found to be 1.13 ± 0.68 bpm and 18.30 ± 10.33 ms respectively. Finally, we also conduct a few experiments to highlight the accuracy improvements made by the proposed algorithms.
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Chan et al. (2023) studied this question. HR-RR-tuned rPPG filter was evaluated on Mean absolute errors of HR and SDNN. An adaptively tuned rPPG filter estimated heart rate and HRV from smartphone video with mean absolute errors of 1.13 ± 0.68 bpm and 18.30 ± 10.33 ms, respectively.
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