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    TFSNet improves remote photoplethysmography (rPPG) by using time-frequency analysis to overcome signal interference and phase discrepancies. This novel approach enhances blood volume pulse estimation and heart rate prediction accuracy and robustness.

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    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Computer Vision

    Background:

    • Remote photoplethysmography (rPPG) estimates blood volume pulse (BVP) from facial videos.
    • Current rPPG methods face limitations due to time-domain processing and phase discrepancies.

    Purpose of the Study:

    • To introduce TFSNet, a novel network for improved rPPG signal estimation and heart rate prediction.
    • To address interference susceptibility and phase supervision issues in existing rPPG techniques.

    Main Methods:

    • Developed TFSNet, a time-frequency synergy network.
    • Implemented a time-frequency fusion (TFF) module to integrate frequency-domain information.
    • Introduced an amplitude-phase decoupling (APD) module for phase compensation.

    Main Results:

    • TFSNet achieved state-of-the-art performance in rPPG signal estimation.
    • Demonstrated significant improvements in accuracy and robustness compared to existing methods.
    • Successfully mitigated interference and phase supervision issues.

    Conclusions:

    • TFSNet offers a robust and accurate solution for rPPG signal estimation and heart rate prediction.
    • The proposed time-frequency synergy approach effectively addresses limitations of current rPPG methods.