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    This study introduces a new method for estimating respiratory rate (RR) using cross-correlation, improving accuracy and reliability. Evaluating estimate quality and using longer signal durations significantly enhance the precision of wearable-based vital sign monitoring.

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    Area of Science:

    • Biomedical Engineering
    • Physiological Monitoring
    • Signal Processing

    Background:

    • Respiratory rate (RR) monitoring is crucial for assessing health conditions.
    • Wearable technology offers non-invasive, continuous vital sign measurement.
    • Existing RR estimation from ECG/PPG signals can be unreliable due to signal quality issues.

    Purpose of the Study:

    • To develop a novel, reliable method for respiratory rate estimation.
    • To introduce a quality metric for RR estimates.
    • To investigate the impact of signal duration and data fusion on estimation accuracy.

    Main Methods:

    • Proposed a novel RR estimation method based on the cross-correlation function.
    • Utilized signal variance as a quality metric for RR estimates.
    • Evaluated two fusion techniques and the effect of signal duration on accuracy.

    Main Results:

    • The novel method achieved 0-bias and LOAs from -5.37 to 5.44 bpm.
    • Quality evaluation using variance improved estimation accuracy.
    • Fusion techniques and longer signal durations further enhanced accuracy, reaching LOAs between -1.44 and 0.9 bpm.

    Conclusions:

    • The proposed cross-correlation method offers improved RR estimation accuracy.
    • Quality assessment and data fusion are critical for reliable vital sign monitoring.
    • Longer signal durations positively correlate with higher estimation accuracy in RR monitoring.