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Motion Artifact Resilient SCG-based Biometric Authentication Using Machine Learning.

Po-Ya Hsu, Po-Han Hsu, Tsung-Han Lee

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Wearable Technology

    Background:

    • Physiological signal biometrics are gaining traction for privacy and health monitoring.
    • Seismocardiogram (SCG) signals are increasingly accessible via wearable sensors.
    • SCG biometrics face challenges due to motion artifacts, limiting exploration.

    Purpose of the Study:

    • To identify optimal sensor placement for SCG biometric authentication.
    • To develop an effective SCG noise removal algorithm.
    • To evaluate machine learning for SCG-based biometric tasks.

    Main Methods:

    • Sensors were placed on wrists, neck, heart, and sternum during various activities (sitting, standing, walking, post-exercise).
    • A novel SCG noise removal algorithm was developed.
    • Machine learning models were employed for biometric authentication.

    Main Results:

    • Vertical and dorsal-ventral SCG signals near the heart and sternum yielded state-of-the-art biometric performance.
    • The noise removal algorithm demonstrated efficacy in authenticating walking motion.
    • The study validated methods on 20 healthy young adults.

    Conclusions:

    • SCG near the heart/sternum offers reliable biometric authentication.
    • The developed noise reduction technique enhances SCG usability during motion.
    • SCG biometrics can enhance privacy and provide clinical cardiovascular insights.