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EMG-Informed Neuromusculoskeletal Models Accurately Predict Knee Loading Measured Using Instrumented Implants.

Kieran J Bennett, Claudio Pizzolato, Saulo Martelli

    IEEE Transactions on Bio-Medical Engineering
    |January 6, 2022
    PubMed
    Summary

    Electromyogram (EMG)-informed modeling significantly improves knee joint loading estimates compared to static optimization. Including EMG data enhances accuracy in musculoskeletal models for better knee load prediction.

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    Area of Science:

    • Biomechanics
    • Musculoskeletal modeling
    • Knee joint loading analysis

    Background:

    • Estimating knee joint loading is crucial for understanding joint health and prosthesis performance.
    • Static optimization (SO) is a common method, but its accuracy in complex joint loading scenarios is debated.
    • Electromyogram (EMG)-informed models offer a potential improvement by incorporating muscle activation data.

    Purpose of the Study:

    • To estimate knee joint loading using static optimization (SO).
    • To explore calibration functions in EMG-informed models for knee load estimation.
    • To determine if EMG-informed stochastic methods can solve muscle redundancy problems in knee loading.

    Main Methods:

    • Generated musculoskeletal models for three individuals with instrumented knee replacements.
    • Calculated muscle forces using SO, EMG-informed, and EMG-informed stochastic methods.
    • Compared prosthesis-measured knee joint loads with model predictions and calculated RMSE.

    Main Results:

    • The EMG-informed stochastic method achieved the lowest RMSE (7-108 N) compared to SO (192-674 N) and calibrated EMG-informed solutions (152-487 N).
    • The stochastic method's solution spaces encompassed measured joint loading up to 98% of stance.
    • EMG-informed modeling demonstrated superior accuracy in estimating knee joint loading.

    Conclusions:

    • Uncertainty in muscle forces can explain total knee loading.
    • EMG measurements should be incorporated into models for more accurate knee joint loading estimation.
    • EMG-informed modeling provides a more precise estimation of knee joint loading than SO.