Abstract
Subject-specific musculoskeletal modeling can be applied to study musculoskeletal disorders, allowing inclusion of personalized anatomy and properties. Independent of the tools used for model creation, there are unavoidable uncertainties associated with parameter identification, whose effect on model predictions is still not fully understood. The aim of the present study was to analyze the sensitivity of subject-specific model predictions (i.e., joint angles, joint moments, muscle and joint contact forces) during walking to the uncertainties in the identification of body landmark positions, maximum muscle tension and musculotendon geometry. To this aim, we created an MRI-based musculoskeletal model of the lower limbs, defined as a 7-segment, 10-degree-of-freedom articulated linkage, actuated by 84 musculotendon units. We then performed a Monte-Carlo probabilistic analysis perturbing model parameters according to their uncertainty, and solving a typical inverse dynamics and static optimization problem using 500 models that included the different sets of perturbed variable values. Model creation and gait simulations were performed by using freely available software that we developed to standardize the process of model creation, integrate with OpenSim and create probabilistic simulations of movement. The uncertainties in input variables had a moderate effect on model predictions, as muscle and joint contact forces showed maximum standard deviation of 0.3 times body-weight and maximum range of 2.1 times body-weight. In addition, the output variables significantly correlated with few input variables (up to 7 out of 312) across the gait cycle, including the geometry definition of larger muscles and the maximum muscle tension in limited gait portions. Although we found subject-specific models not markedly sensitive to parameter identification, researchers should be aware of the model precision in relation to the intended application. In fact, force predictions could be affected by an uncertainty in the same order of magnitude of its value, although this condition has low probability to occur.
Highlights
Advances in computing power and numerical methods for modeling and simulation of movement are expanding the use of computational models of the musculoskeletal system in research and clinical applications [1,2]
We analyzed the sensitivity of the predictions of an MRI-based musculoskeletal model during walking to the unavoidable uncertainties in parameter identification, i.e., body landmark positions, maximum muscle tension and musculotendon geometry (Figure 1)
The unavoidable uncertainties in parameter identification during the process of model creation have a moderate effect on model predictions during gait
Summary
Advances in computing power and numerical methods for modeling and simulation of movement are expanding the use of computational models of the musculoskeletal system in research and clinical applications [1,2]. Because musculoskeletal geometry and tissue properties can vary markedly among individuals, the accuracy of generic models has been questioned [5,6], when studying musculoskeletal disorders [7,8]. Analyses of musculoskeletal dynamics require the use of musculoskeletal models and the application of rigid body dynamics and optimization methods to calculate muscle forces [2,15]. The process involves the definition and calculation of subject-specific modeling parameters from imaging data, including the identification of: tissue volumes and densities to calculate body inertial properties; body landmark positions to define joint reference frames and constraints; muscle attachment points to define the geometry of muscles; and muscle architecture parameters to calculate muscle force-generating capacities
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