Fast Muscle-parameter Calibration using EMG and Markerless Kinematics for Neuromusculoskeletal Modeling: Application to Hand-cycling

Objective: Estimating personalized muscle forces through musculoskeletal modeling is valuable for assessing patient status and monitoring clinical progress. However, this process involves numerous model parameters that are difficult to measure. Upper-limb applications are particularly limited due to the complexity of the system and the long computation times required for model calibration. This study proposes a rapid (<5 min) calibration method for upper-limb musculoskeletal models. Methods: We calibrated maximal isometric force and optimal muscle length for 38 muscles across 10 degrees of freedom by matching muscle-generated moments with dynamically consistent joint moments. The method leverages experimental data including bony landmark trajectories from markerless motion capture, external forces, and electromyography (EMG). Results: Joint moment estimation and calibration were completed together in less than five minutes. During hand-cycling, the calibrated model reduced EMG tracking error compared to the uncalibrated model (5.58±0.92% vs. 6.30±1.28%). Reliance on non-physiological residual moments was also lowered (12.68 vs. 23.61% of peak moment for calibrated vs. uncalibrated models, respectively). Conclusion: The proposed method enables rapid calibration of upper-limb muscle parameters, improving accuracy in muscle force estimation and reducing dependence on residual moments. Significance: This approach provides a fast and reliable framework for upper-limb musculoskeletal calibration, facilitating more accurate and clinically applicable muscle force estimation.