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This study introduces a rapid method for calibrating upper-limb musculoskeletal models, significantly improving muscle force estimation accuracy. The new approach reduces computation time and reliance on non-physiological moments for clinical applications.

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Area of Science:

Biomechanics
Musculoskeletal modeling
Human movement analysis

Background:

Personalized muscle force estimation is crucial for patient assessment and clinical monitoring.
Musculoskeletal modeling faces challenges with parameter measurement and long calibration times, especially for complex upper-limb systems.
Existing methods often rely on non-physiological residual moments, limiting accuracy.

Purpose of the Study:

To develop and validate a rapid (<5 min) calibration method for upper-limb musculoskeletal models.
To improve the accuracy of personalized muscle force estimation.
To reduce the computational burden and reliance on residual moments in musculoskeletal modeling.

Main Methods:

Calibrated maximal isometric force and optimal muscle length for 38 muscles across 10 degrees of freedom.
Matched muscle-generated moments with dynamically consistent joint moments.
Utilized markerless motion capture, external forces, and electromyography (EMG) data.

Main Results:

Achieved joint moment estimation and calibration in under five minutes.
Reduced electromyography (EMG) tracking error by 11% compared to an uncalibrated model during hand-cycling.
Lowered the reliance on non-physiological residual moments by over 46%.

Conclusions:

The proposed method enables fast and accurate calibration of upper-limb musculoskeletal models.
Improved muscle force estimation and reduced dependence on residual moments.
Offers a reliable framework for clinically applicable musculoskeletal analysis.