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

Abstract

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 ($< $5 min) calibration method for upper-limb musculoskeletal models. We calibrated maximal isometric force and optimal muscle length for 38 muscles across 10 degrees of freedom by matching muscle-generated moments with dynamically consistent joint moments. The method leverages experimental data including bony landmark trajectories from markerless motion capture, external forces, and electromyography (EMG). Joint moment estimation and calibration were completed together in less than five minutes. During hand-cycling, the calibrated model reduced EMG tracking error compared to the uncalibrated model (5.58$\pm$0.92% vs. 6.30$\pm$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). The proposed method enables rapid calibration of upper-limb muscle parameters, improving accuracy in muscle force estimation and reducing dependence on residual moments. This approach provides a fast and reliable framework for upper-limb musculoskeletal calibration, facilitating more accurate and clinically applicable muscle force estimation.