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 rPPG filtering adaptively tuned by HR and respiratory rate (RR) to better enhance source signal that modulates HR. Further, we also addressed the smartphone artifact— occasionally seen in smartphone videos—by introducing an algorithm designed to suppress these artifacts. 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 1.36 ± 0.88 bpm and 23.47 ± 12.09 ms respectively. Finally, we also conducted experiments to highlight improvements in accuracies made by the proposed algorithms.