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    This study introduces a new method for smartwatch respiratory rate (RR) estimation using Frequency Domain Peak (FDP) analysis. The approach significantly improves accuracy and reduces errors compared to existing techniques.

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    Area of Science:

    • Biomedical Engineering
    • Wearable Technology
    • Signal Processing

    Background:

    • Photoplethysmogram (PPG) sensors in smartwatches offer a convenient way to estimate Respiratory Rate (RR).
    • Current algorithms for RR extraction from PPG are often inaccurate and vulnerable to noise and motion artifacts.
    • Existing methods may not efficiently handle the complexities of physiological signals captured by wearables.

    Purpose of the Study:

    • To develop a more accurate and robust algorithm for Respiratory Rate (RR) estimation from smartwatch Photoplethysmogram (PPG) data.
    • To address the limitations of existing methods concerning accuracy, noise, and movement artifacts.
    • To evaluate the efficacy of Frequency Domain Peak (FDP) analysis utilizing the Frequency Modulation (FM) feature.

    Main Methods:

    • Proposed a novel algorithm based on Frequency Domain Peak (FDP) analysis using the Frequency Modulation (FM) feature of PPG signals.
    • Analyzed and compared the performance against existing methods, including Smart Fusion (SFU).
    • Focused on signal processing techniques to enhance accuracy and reduce susceptibility to artifacts.

    Main Results:

    • The proposed FDP method demonstrated a significant improvement in the Figure of Merit (FoM), exceeding 130%.
    • Achieved a mean error reduction of over 60% compared to existing algorithms.
    • Found that Smart Fusion (SFU) offers minimal benefits despite increased computational cost, suggesting its avoidance.

    Conclusions:

    • The novel Frequency Domain Peak (FDP) analysis using Frequency Modulation (FM) offers a more accurate and efficient solution for smartwatch-based Respiratory Rate (RR) estimation.
    • Avoiding computationally expensive and minimally beneficial techniques like Smart Fusion (SFU) is recommended for improved performance and efficiency.
    • The proposed method is reliable and suitable for a broad spectrum of applications requiring accurate RR monitoring.