Background: Remote photoplethysmography (rPPG) has been in the forefront recently for measuring cardiac pulse rates from live or recorded videos. It finds advantages in scenarios requiring remote monitoring, such as medicine and fitness, where contact based monitoring is limiting and cumbersome. The blood volume pulse, defined as the pulsative flow of arterial blood, gives rise to periodic changes in the skin color which are then quantified to estimate a temporal signal. This temporal signal can be analysed using various methods to extract the representative cardiac signal.
Methods: We present a novel method for measuring rPPG signals using constrained independent component analysis (cICA). We incorporate a priori information into the cICA algorithm to aid in the extraction of the most prominent rPPG signal. This a priori information is implemented using two constraints: first, based on periodicity using autocorrelation, and second, a chrominance-based constraint exploiting the physical characteristics of the skin.
Results and conclusion: Our method showed improved performances over traditional blind source separation methods like ICA and chrominance based methods with mean absolute errors of 0.62 beats per minute (BPM) and 3.14 BPM for the two datasets in our inhouse video database UBFC-RPPG, and 4.69 BPM for the public MMSE-HR dataset. Its performance was also better in comparison to other state of the art methods in terms of accuracy and robustness. Our UBFC-RPPG database is also made publicly available and is specifically aimed towards testing rPPG measurements.
Keywords: Chrominance constraint; Constrained independent component analysis; Periodicity constraint; Remote photoplethysmography; Semiblind source separation.