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Quantifying the Effects of Increasing Mechanical Stress on Knee Acoustical Emissions Using Unsupervised Graph Mining.

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    Knee joint sounds, or acoustical emissions, increase in complexity with greater mechanical stress. This finding, using graph mining, could help monitor knee loading and aid rehabilitation.

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    Area of Science:

    • Biomechanics
    • Acoustics
    • Data Mining

    Background:

    • Mechanical stress on knee joints can lead to injury.
    • Assessing knee joint loading is crucial for rehabilitation and injury prevention.
    • Non-invasive methods for monitoring joint loading are needed.

    Purpose of the Study:

    • To investigate the relationship between mechanical stress and knee acoustical emissions.
    • To analyze knee sounds using unsupervised graph mining.
    • To develop a method for quantifying joint loading based on acoustic signals.

    Main Methods:

    • Miniature contact microphones were placed on four knee locations (patella, meniscus).
    • Audio features were extracted from acoustical signals.
    • A graph community factor (GCF) was calculated using k-nearest neighbor graphs and Infomap community detection.

    Main Results:

    • The GCF significantly increased with vertical loading forces in 12 healthy subjects.
    • Increased sound complexity correlated with increased joint forces.
    • Medial patella and lateral meniscus microphone placements showed the most sound variation.

    Conclusions:

    • Knee acoustical emissions complexity reflects joint loading.
    • The GCF can quantify knee joint loading.
    • Optimal microphone placement can enhance sensitivity to loading for future applications in rehabilitation and activity monitoring.