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    This study introduces new smartphone sensor algorithms, complementary filter (CFF) and Kalman filter with gradient descent (KFGD), to improve orientation estimation for telerehabilitation. KFGD offers stable and accurate motion tracking, outperforming existing methods for elderly exercise and sports injury recovery.

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    Area of Science:

    • Biomedical Engineering
    • Sensor Technology
    • Rehabilitation Science

    Background:

    • Smartphones offer potential for telerehabilitation due to their accessibility.
    • Accurate orientation estimation using smartphone sensors is challenging, particularly due to gyroscope drift.
    • Existing motion tracking methods often require specialized, costly hardware.

    Purpose of the Study:

    • To develop and evaluate novel algorithms for accurate smartphone-based orientation estimation.
    • To address the challenge of gyroscope drift in mobile inertial sensor data.
    • To assess the suitability of these algorithms for telerehabilitation exercises.

    Main Methods:

    • A complementary filter (CFF) was developed to reduce gyroscope integration errors.
    • A novel orientation estimation algorithm, Kalman filter with gradient descent (KFGD), was proposed.
    • Performance was evaluated on two early-stage rehabilitation exercises, comparing against commercial systems (XSENS Awinda) and the Madgwick algorithm.

    Main Results:

    • CFF demonstrated fast motion tracking and effectively corrected gyroscope integration errors.
    • KFGD showed comparable accuracy to XSENS Awinda and superior stability and performance compared to CFF.
    • KFGD outperformed the Madgwick algorithm when using mobile sensor data.

    Conclusions:

    • The proposed KFGD algorithm is suitable for low-cost mobile inertial sensors in telerehabilitation.
    • KFGD provides stable and accurate orientation estimation, beneficial for early recovery stages of sports injuries.
    • This technology can enhance exercise for the elderly and remote patient monitoring.