IMPROVING HEART RATE AND HEART RATE VARIABILITY ESTIMATION FROM VIDEO THROUGH A HR-RR-TUNED FILTER

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.