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Research Papers

Neuromusculoskeletal Model Calibration Significantly Affects Predicted Knee Contact Forces for Walking

[+] Author and Article Information
Gil Serrancolí

Department of Mechanical Engineering and
Biomedical Engineering Research Centre,
Universitat Politècnica de Catalunya,
Barcelona, Catalunya 08028, Spain
e-mail: gilserrancoli@hotmail.com

Allison L. Kinney

Department of Mechanical and
Aerospace Engineering,
University of Dayton,
Dayton, OH 45469
e-mail: akinney2@udayton.edu

Benjamin J. Fregly

Department of Mechanical and
Aerospace Engineering,
University of Florida,
Gainesville, FL 32611
e-mail: fregly@ufl.edu

Josep M. Font-Llagunes

Department of Mechanical Engineering and
Biomedical Engineering Research Centre,
Universitat Politècnica de Catalunya,
Av. Diagonal 647,
Barcelona, Catalunya 08028, Spain
e-mail: josep.m.font@upc.edu

1Corresponding author.

Manuscript received May 24, 2015; final manuscript received May 10, 2016; published online June 13, 2016. Assoc. Editor: Silvia Blemker.

J Biomech Eng 138(8), 081001 (Jun 13, 2016) (11 pages) Paper No: BIO-15-1258; doi: 10.1115/1.4033673 History: Received May 24, 2015; Revised May 10, 2016

Though walking impairments are prevalent in society, clinical treatments are often ineffective at restoring lost function. For this reason, researchers have begun to explore the use of patient-specific computational walking models to develop more effective treatments. However, the accuracy with which models can predict internal body forces in muscles and across joints depends on how well relevant model parameter values can be calibrated for the patient. This study investigated how knowledge of internal knee contact forces affects calibration of neuromusculoskeletal model parameter values and subsequent prediction of internal knee contact and leg muscle forces during walking. Model calibration was performed using a novel two-level optimization procedure applied to six normal walking trials from the Fourth Grand Challenge Competition to Predict In Vivo Knee Loads. The outer-level optimization adjusted time-invariant model parameter values to minimize passive muscle forces, reserve actuator moments, and model parameter value changes with (Approach A) and without (Approach B) tracking of experimental knee contact forces. Using the current guess for model parameter values but no knee contact force information, the inner-level optimization predicted time-varying muscle activations that were close to experimental muscle synergy patterns and consistent with the experimental inverse dynamic loads (both approaches). For all the six gait trials, Approach A predicted knee contact forces with high accuracy for both compartments (average correlation coefficient r = 0.99 and root mean square error (RMSE) = 52.6 N medial; average r = 0.95 and RMSE = 56.6 N lateral). In contrast, Approach B overpredicted contact force magnitude for both compartments (average RMSE = 323 N medial and 348 N lateral) and poorly matched contact force shape for the lateral compartment (average r = 0.90 medial and −0.10 lateral). Approach B had statistically higher lateral muscle forces and lateral optimal muscle fiber lengths but lower medial, central, and lateral normalized muscle fiber lengths compared to Approach A. These findings suggest that poorly calibrated model parameter values may be a major factor limiting the ability of neuromusculoskeletal models to predict knee contact and leg muscle forces accurately for walking.

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Figures

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Fig. 1

Block diagram of the two-step optimization formulation. asyn stands for activations reconstructed from synergy components, ma and madev for moment arm and moment arm deviations, respectively, sa for activation scale factors for muscles with experimental EMG data, SVmod for synergy vectors for muscles without experimental EMG data, l0M and sl0M for optimal fiber lengths and their scale factors, lsT and slsT for tendon slack lengths and their scale factors, a for model activations, ares for reserve activations, Fs for reserve actuator strength values (which are 0.5 Nm), athres for half-range of allowable activation variation (0.01 for muscles with associated experimental EMG data and 0.05 for all other muscles), f for muscle forces, and M for inverse dynamic moments. i is the muscle (44 muscles), j is the time frame (101 frames), and k is the tracked joint moment (six loads).

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Fig. 5

Activations reconstructed from synergy components (activationSyn in solid lines) and model activations (activation in dashed lines) for muscles with associated experimental EMG data in one representative gait cycle. Asterisks (*) indicate statistically different r values between approaches A and B.

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Fig. 4

Normalized muscle fiber lengths for muscles with the greatest differences in mean muscle forces between approaches A and B. The plotted area corresponds to the mean ± 1 standard deviation for all six gait cycles.

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Fig. 3

Muscle forces for muscles with the greatest mean differences between approaches A and B. The plotted area corresponds to the mean ± standard deviation for all six gait cycles.

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Fig. 2

Experimental knee contact forces and mean knee contact force predictions for approaches A and B. The gray area corresponds to the mean ± standard deviation for the experimental forces.

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