Consistency Among Musculoskeletal Models: Caveat Utilitor

Wagner, Daniel A.; Stepanyan, Vahagn; Shippen, James; DeMers, Matthew S.; Gibbons, Robin; Andrews, B.J.; Creasey, Graham; Beaupré, Gary S.

doi:10.1007/s10439-013-0843-1

Cited by 38 publications

(22 citation statements)

References 59 publications

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“…Calculation of muscle and joint forces represent a challenging modeling application [3], [4]. Because musculoskeletal geometry and tissue properties can vary markedly among individuals, the accuracy of generic models has been questioned [5], [6], particularly when studying musculoskeletal disorders [7], [8]. Conversely, subject-specific models allow inclusion of individual musculoskeletal anatomy and properties, providing an alternative approach to calculating muscle moment arms [9], [10], muscle and joint forces [11], [12], bone and cartilage stresses [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Are Subject-Specific Musculoskeletal Models Robust to the Uncertainties in Parameter Identification?

Valente

Pitto

Testi³

et al. 2014

PLoS ONE

158

176

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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.

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Section: Introductionmentioning

confidence: 99%

Are Subject-Specific Musculoskeletal Models Robust to the Uncertainties in Parameter Identification?

Valente

Pitto

Testi³

et al. 2014

PLoS ONE

158

176

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“…As discussed by Wagner and colleagues (Wagner et al 2013), the ability to derive consistent results from generic musculoskeletal models implemented by different researchers in different platforms is a critical need for the advancement of our field. Across new platforms, we expect that the benchmarking simulations provided here will help future users evaluate the consistency of their results in the context of our original implementation of the model.…”

Section: Discussionmentioning

confidence: 99%

“…Computational approaches to deriving inputs and calculating the dynamic behavior of a musculoskeletal system are conceptually similar across platforms, and include common modeling and simulation components such as descriptions of joint kinematics and range of motion, damping, passive and active force generators, dynamics engines to solve the equations of motion, and numerical integrators. Despite the conceptual similarities, technical differences in the implementation of computational algorithms can influence simulation output (Wagner et al 2013). In addition, direct replication of published simulation results remains a challenge for our community for a number of reasons.…”

Section: Introductionmentioning

confidence: 99%

“…Next, even when models and platforms are available, there is often a financial expense associated with acquiring multiple different platforms. While transferring published models and control signal inputs from one platform to another may eliminate this financial burden, it also incurs the technical expense of gaining the expertise needed to run complex simulations in multiple systems (Wagner et al 2013). …”

Section: Introductionmentioning

confidence: 99%

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Benchmarking of dynamic simulation predictions in two software platforms using an upper limb musculoskeletal model

Saul

Hu²,

Goehler

et al. 2014

Computer Methods in Biomechanics and Biomedical Engineering

275

276

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Several opensource or commercially available software platforms are widely used to develop dynamic simulations of movement. While computational approaches are conceptually similar across platforms, technical differences in implementation may influence output. We present a new upper limb dynamic model as a tool to evaluate potential differences in predictive behavior between platforms. We evaluated to what extent differences in technical implementations in popular simulation software environments result in differences in kinematic predictions for single and multijoint movements using EMG- and optimization-based approaches for deriving control signals. We illustrate the benchmarking comparison using SIMM-Dynamics Pipeline-SD/Fast and OpenSim platforms. The most substantial divergence results from differences in muscle model and actuator paths. This model is a valuable resource and is available for download by other researchers. The model, data, and simulation results presented here can be used by future researchers to benchmark other software platforms and software upgrades for these two platforms.

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“…Moreover, it is difficult to estimate the co-contraction of agonist and antagonist muscles using a musculoskeletal model. This is because an optimization scheme for the approximate minimization of muscle stress is typically used to estimate muscle activation patterns in musculoskeletal models [18]. Antagonist muscle activation increases the metabolic cost and is not selected by such schemes.…”

Section: Introductionmentioning

confidence: 99%

Estimating Co-Contraction Activation of Trunk Muscles Using a Novel Musculoskeletal Model for Pregnant Women

Morino

Takahashi

2017

Applied Sciences

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Weight gain and stretched abdominal muscles from an enlarged gravid uterus are remarkable features during pregnancy. These changes elicit postural instability and place strain on body segments, contributing to lower back pain. In general, the agonist and antagonist muscles act simultaneously to increase joint stabilization; however, this can cause additional muscle stress during movement. Furthermore, this activation can be observed in pregnant women because of their unstable body joints. Hence, physical modalities based on assessments of muscle activation are useful for managing low back pain during pregnancy. Musculoskeletal models are common when investigating muscle load. However, it is difficult to apply such models to pregnant women and estimate the co-contraction of muscles using musculoskeletal models. Therefore, the purpose of this study is to construct a musculoskeletal model for pregnant women that estimates the co-contraction of trunk muscles. First, motion analysis was conducted on a pregnant woman and the muscle activations of the rectus abdominis and erector spinae were measured. Then, the musculoskeletal model was specifically modified for pregnant women. Finally, the co-contraction was estimated from the results of the musculoskeletal model and electromyography data using a genetic algorithm. With the proposed methods, weakened abdominal muscle torque and the co-contraction activation of trunk muscles were estimated successfully.

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Consistency Among Musculoskeletal Models: Caveat Utilitor

Cited by 38 publications

References 59 publications

Are Subject-Specific Musculoskeletal Models Robust to the Uncertainties in Parameter Identification?

Are Subject-Specific Musculoskeletal Models Robust to the Uncertainties in Parameter Identification?

Benchmarking of dynamic simulation predictions in two software platforms using an upper limb musculoskeletal model

Estimating Co-Contraction Activation of Trunk Muscles Using a Novel Musculoskeletal Model for Pregnant Women

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