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

Objective: Using a musculoskeletal modelling framework, we aimed to (1) estimate knee joint loading using static optimization (SO); (2) explore different calibration functions in electromyogram (EMG)-informed models used in estimating knee load; and (3) determine, when using an EMG-informed stochastic method, if the measured joint loadings are solutions to the muscle redundancy problem when investigating only the uncertainty in muscle forces. Methods: Musculoskeletal models for three individuals with instrumented knee replacements were generated. Muscle forces were calculated using SO, EMG-informed, and EMG-informed stochastic methods. Measured knee joint loads from the prostheses were compared to the SO and EMG-informed solutions. Root mean square error (RMSE) in joint load estimation was calculated, and the muscle force ranges were compared. Results: The RMSE ranged between 192-674 N, 152-487 N, and 7-108 N for the SO, the calibrated EMG-informed solution, and the best fit stochastic result, respectively. The stochastic method produced solution spaces encompassing the measured joint loading up to 98% of stance. Conclusion: Uncertainty in muscle forces can account for total knee loading and it is recommended that, where possible, EMG measurements should be included to estimate knee joint loading. Significance: This work shows that the inclusion of EMG-informed modelling allows for better estimation of knee joint loading when compared to SO.

Automated summary

This study used a musculoskeletal modelling framework to compare different methods for estimating knee joint loading, and found that using EMG-informed modelling allows for better estimation of knee joint loading.