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Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation
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Predicting Free Achilles Tendon Strain From Motion Capture Data Using Artificial Intelligence.

Zhengliang Xia, Daniel Devaprakash, Bradley M Cornish

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |July 17, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an AI workflow to estimate Achilles tendon strain during running using motion capture data. The AI model accurately predicts strain, enabling easier in-field assessments for athletes and researchers.

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    Area of Science:

    • Biomechanics
    • Artificial Intelligence
    • Sports Science

    Background:

    • Achilles tendon (AT) mechanical properties improve with appropriate strain.
    • Estimating free AT strain typically requires time-consuming neuromusculoskeletal (NMSK) modeling and lab data.
    • In-field AT strain assessment is needed for practical applications.

    Purpose of the Study:

    • To develop and validate an artificial intelligence (AI) workflow for predicting free Achilles tendon strain during running using motion capture data.
    • To compare the performance of two AI workflows: direct strain prediction (LSTM only) versus force-mediated strain prediction (LSTM+).
    • To assess the impact of input features on prediction accuracy for in-field applications.

    Main Methods:

    • Developed two AI workflows (LSTM only, LSTM+) using synthesized motion capture keypoint data with added noise.
    • Trained and evaluated AI models against free AT strain estimates from a validated NMSK model.
    • Investigated the influence of keypoint position, velocity, acceleration, and participant height/mass on strain prediction.

    Main Results:

    • The LSTM+ workflow significantly outperformed the LSTM only workflow (p < 0.001) in predicting free AT strain.
    • Optimal predictions were achieved using keypoint positions/velocities and participant height/mass.
    • Achieved average time-series RMSE of 1.72±0.95% strain (r2=0.92±0.10) and peak strain RMSE of 2.20% (r2=0.54).

    Conclusions:

    • An AI workflow can accurately predict free Achilles tendon strain during running from low-fidelity pose estimation data.
    • The LSTM+ approach, predicting force then strain, is superior for AT strain estimation.
    • This AI-driven method facilitates feasible in-field assessments of Achilles tendon loading.