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Related Experiment Video

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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
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Heart Rate Estimation Through Autocorrelation from Single Axis Accelerometer of Smartphone.

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    |December 3, 2025
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    This study presents a novel mobile phone method for predicting heart rate (HR) using accelerometer data. The technique shows high accuracy for Sinus Rhythm but requires further development for Atrial Fibrillation.

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    Area of Science:

    • Biomedical Engineering
    • Mobile Health Technology
    • Cardiovascular Monitoring

    Background:

    • Smartphones are increasingly used for health monitoring due to their widespread adoption.
    • Accurate heart rate (HR) monitoring is crucial for diagnosing and managing cardiac conditions.

    Purpose of the Study:

    • To develop and evaluate an autocorrelation-based technique for predicting heart rate from single-axis accelerometer data on mobile phones.
    • To assess the accuracy of this method in individuals with Sinus Rhythm (SR) and Atrial Fibrillation (AF).

    Main Methods:

    • Utilized mobile phone accelerometer data and applied Butterworth and Bessel filters for signal preprocessing.
    • Developed an autocorrelation-based algorithm to extract the cardiac signal and calculate HR.
    • Validated the technique using simultaneous accelerometer and ECG data from 300 individuals (SR and AF).

    Main Results:

    • The method achieved a Mean Absolute Error (MAE) of 4.54 Beats Per Minute (BPM) for subjects in Sinus Rhythm, meeting clinical accuracy standards.
    • A higher MAE of 15.7 BPM was observed in subjects with Atrial Fibrillation, indicating lower accuracy in arrhythmic populations.

    Conclusions:

    • The proposed mobile phone-based technique shows promise for accurate heart rate prediction in individuals with normal heart rhythms.
    • Further research and refinement are necessary to improve the accuracy of this method for patients with cardiac arrhythmias like Atrial Fibrillation.