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A Real-Time Gait Phase Detection Method Based on BiLSTM-Attention Model.

Shaochen Xu, Hongtao Dong, Rui Xu

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    Summary

    This study introduces a BiLSTM-Attention model for real-time gait phase detection, crucial for intelligent rehabilitation. The method accurately identifies walking phases with minimal delay, aiding patients with motor disorders.

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    Area of Science:

    • Biomedical Engineering
    • Rehabilitation Technology
    • Machine Learning in Healthcare

    Background:

    • Accurate real-time gait phase detection is critical for effective walking assistance in rehabilitation for motor disorders.
    • Existing methods may lack the precision or speed required for seamless integration into intelligent training systems.

    Purpose of the Study:

    • To develop and validate an efficient real-time gait phase detection method using a Bidirectional Long Short-Term Memory network with an Attention layer (BiLSTM-Attention).
    • To assess the model's accuracy and time delay for practical application in gait rehabilitation.

    Main Methods:

    • Utilized a single Inertial Measurement Unit (IMU) on the shank to collect acceleration and angular velocity data during treadmill walking.
    • Employed a sliding window technique to segment data into sequences and trained a BiLSTM-Attention model on data from eight healthy subjects.

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  • Data was partitioned into training, validation, and test sets with a 5:1:2 ratio.

    • Main Results:

    • The BiLSTM-Attention model achieved an average recognition accuracy of 97.40% for detecting three gait phases (loading response, stance, swing) on new subjects.
    • The method demonstrated a low average time delay of 15.7±10.1ms, indicating real-time applicability.
    • Validation on a public dataset confirmed the model's effectiveness.

    Conclusions:

    • The proposed BiLSTM-Attention method offers a highly accurate and efficient solution for real-time gait phase detection.
    • This technology holds significant potential for enhancing the stability and effectiveness of walking assistance in intelligent rehabilitation training.
    • The minimal time delay suggests suitability for practical, real-time clinical applications.