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    This study introduces a novel method for accurate remote physiological measurements using multiple cameras, overcoming challenges posed by head motion and missing data for reliable pulse rate and variability analysis.

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    Area of Science:

    • Physiological monitoring
    • Computer vision
    • Signal processing

    Background:

    • Remote photoplethysmography (PPG) enables noncontact physiological measurements.
    • Head motion and occlusions cause data loss, hindering accurate pulse rate variability (PRV) calculations.
    • Existing methods struggle with missing data from moving subjects.

    Purpose of the Study:

    • To develop and validate a robust method for noncontact PPG measurements from moving subjects.
    • To fuse data from multiple cameras to mitigate the impact of missing observations.
    • To improve the accuracy of pulse rate and PRV estimation in the presence of head motion.

    Main Methods:

    • An array of cameras captures partial color-channel signals.
    • Fusion of signals from multiple cameras to reconstruct physiological data.
    • Systematic testing on 25 subjects across various head motion tasks (25 hours total).
    • Evaluation of pulse rate and PRV parameter estimation accuracy.

    Main Results:

    • Median absolute error in pulse rate measurement was 0.57 BPM.
    • Error in pulse rate was within 0.4 BPM of contact PPG devices for most tasks.
    • PRV estimation error was reduced by over 50% compared to methods not handling missing data.

    Conclusions:

    • The proposed multi-camera fusion approach significantly improves the accuracy of remote physiological measurements.
    • This method enables reliable pulse rate and PRV estimation even with substantial head motion and data loss.
    • It overcomes limitations of single-camera systems and enhances the feasibility of noncontact physiological monitoring.