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Photoplethysmographic imaging is an optical solution for non-contact cardiovascular monitoring from a distance. This camera-based technology enables physiological monitoring in situations where contact-based devices may be problematic or infeasible, such as ambulatory, sleep, and multi-individual monitoring. However, automatically extracting the blood pulse waveform signal is challenging due to the unknown mixture of relevant (pulsatile) and irrelevant pixels in the scene. Here, we propose a signal fusion framework, FusionPPG, for extracting a blood pulse waveform signal with strong temporal fidelity from a scene without requiring anatomical priors. The extraction problem is posed as a Bayesian least squares fusion problem, and solved using a novel probabilistic pulsatility model that incorporates both physiologically derived spectral and spatial waveform priors to identify pulsatility characteristics in the scene. Evaluation was performed on a 24-participant sample with various ages (9-60 years) and body compositions (fat% 30.0 ± 7.9, muscle% 40.4 ± 5.3, BMI 25.5 ± 5.2 kg·m -2 ). Experimental results show stronger matching to the ground-truth blood pulse waveform signal compared to the FaceMeanPPG ( p &lt; 0.001) and DistancePPG ( p &lt; 0.001) methods. Heart rates predicted using FusionPPG correlated strongly with ground truth measurements ( r  2 = 0.9952). A cardiac arrhythmia was visually identified in FusionPPG's waveform via temporal analysis.

Spectral-spatial fusion model for robust blood pulse waveform extraction in photoplethysmographic imaging