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Related Experiment Video

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Rehabilitation Exercise Segmentation for Autonomous Biofeedback Systems with ConvFSM.

Antonio Bevilacqua, Louise Brennan, Rob Argent

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 18, 2020
    PubMed
    Summary

    This study introduces ConvFSM, a new method using Convolutional Neural Networks and Finite State Machines for segmenting physical movements in rehabilitation. It effectively identifies motion primitives from sensor data with minimal domain expertise.

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    Area of Science:

    • Biomedical Engineering
    • Rehabilitation Technology
    • Machine Learning in Healthcare

    Background:

    • Accurate segmentation of physical movements is crucial for accelerometry-based biofeedback systems in physiotherapy.
    • Existing methods often require extensive domain knowledge and struggle with complex or novel exercises.
    • Inertial Measurement Units (IMUs) offer a promising avenue for capturing movement data.

    Purpose of the Study:

    • To develop and evaluate a novel technique for segmenting upper limb rehabilitation exercises using IMUs.
    • To introduce a method that minimizes the need for domain-specific knowledge in motion primitive detection.
    • To explore optimal sensor configurations for home-based rehabilitation feedback systems.

    Main Methods:

    • A new segmentation technique, Convolutional Neural Networks and Finite State Machines (ConvFSM), was developed.
    • ConvFSM utilizes Convolutional Neural Networks (CNNs) and Finite State Machines (FSMs) to identify motion primitives.
    • The study investigated various sensor combinations for effectiveness and flexibility in home-based settings.

    Main Results:

    • ConvFSM successfully isolated motion primitives from raw streaming inertial data.
    • The technique demonstrated the ability to segment exercises with very little domain knowledge.
    • Experimental results were validated using a dataset comprising upper and lower limb exercises.

    Conclusions:

    • The ConvFSM technique offers an effective and adaptable solution for segmenting rehabilitation exercises.
    • This approach has the potential to enhance autonomous biofeedback systems for physiotherapy.
    • The findings support the use of IMUs and advanced machine learning for accessible, home-based rehabilitation.