Sensitivity of dynamic simulations of gait and dynamometer experiments to hill muscle model parameters of knee flexors and extensors

Groote, Friedl De; Campen, Anke Van; Schutter, Joris De

doi:10.1016/j.jbiomech.2010.03.022

Cited by 96 publications

(90 citation statements)

References 22 publications

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“…In general, however, a muscle's function was most sensitive to changes in the values of tendon slack length. This finding extends the results of previous investigations which have shown that calculations of muscle forces and joint torques for walking are sensitive to changes in the values assumed for tendon slack length (Redl et al, 2007;De Groote et al, 2010). Unfortunately, in practice, direct measurement of tendon slack length is problematic due to the difficulty in identifying the aponeurotic part of the tendon from the muscle belly, and because the tendon may be elongated when post-mortem measurements are made.…”

Section: Discussionsupporting

confidence: 76%

“…Previous studies have found that muscle-tendon force and muscle-generated torque are most sensitive to changes in tendon slack length (Scovil and Ronsky, 2006;Redl et al, 2007). Muscle-tendon force may also be sensitive to changes in the peak isometric force and corresponding fiber length of muscle (De Groote et al, 2010), depending on the specific muscle under consideration (Xiao and Higginson, 2010).…”

Section: For a Review)mentioning

confidence: 99%

See 1 more Smart Citation

Sensitivity of model predictions of muscle function to changes in moment arms and muscle–tendon properties: A Monte-Carlo analysis

Ackland¹,

Lin²,

Pandy³

2012

Journal of Biomechanics

158

105

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Section: Discussionsupporting

confidence: 76%

Section: For a Review)mentioning

confidence: 99%

Sensitivity of model predictions of muscle function to changes in moment arms and muscle–tendon properties: A Monte-Carlo analysis

Ackland¹,

Lin²,

Pandy³

2012

Journal of Biomechanics

158

105

View full text Add to dashboard Cite

“…Optimal fiber lengths must be carefully examined in the calibration and validation process. Optimal fiber length varies by subject and within regions of each muscle [96] and muscle force predictions have been shown to be sensitive to the optimal fiber length during gait [109,[117][118][119], and likely most other motions.…”

Section: Validating and Evaluating The Robustness Of Muscletendon Dynmentioning

confidence: 99%

“…[7]). It has also been shown that, of all the musculotendon model parameters, tendon slack length has the largest effect on predictions of muscle forces [85,109,118,119]. Changing the tendon slack length alters where muscles operate on the force-length curve, affects the joint angle where peak force is generated, and changes the range over which a muscle can generate force.…”

Section: F-l-v Properties and Passive Stiffness Of Musclesmentioning

confidence: 99%

Is My Model Good Enough? Best Practices for Verification and Validation of Musculoskeletal Models and Simulations of Movement

Hicks

Uchida

Seth

et al. 2015

Journal of Biomechanical Engineering

578

455

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Computational modeling and simulation of neuromusculoskeletal (NMS) systems enables researchers and clinicians to study the complex dynamics underlying human and animal movement. NMS models use equations derived from physical laws and biology to help solve challenging real-world problems, from designing prosthetics that maximize running speed to developing exoskeletal devices that enable walking after a stroke. NMS modeling and simulation has proliferated in the biomechanics research community over the past 25 years, but the lack of verification and validation standards remains a major barrier to wider adoption and impact. The goal of this paper is to establish practical guidelines for verification and validation of NMS models and simulations that researchers, clinicians, reviewers, and others can adopt to evaluate the accuracy and credibility of modeling studies. In particular, we review a general process for verification and validation applied to NMS models and simulations, including careful formulation of a research question and methods, traditional verification and validation steps, and documentation and sharing of results for use and testing by other researchers. Modeling the NMS system and simulating its motion involves methods to represent neural control, musculoskeletal geometry, muscle-tendon dynamics, contact forces, and multibody dynamics. For each of these components, we review modeling choices and software verification guidelines; discuss variability, errors, uncertainty, and sensitivity relationships; and provide recommendations for verification and validation by comparing experimental data and testing robustness. We present a series of case studies to illustrate key principles. In closing, we discuss challenges the community must overcome to ensure that modeling and simulation are successfully used to solve the broad spectrum of problems that limit human mobility.

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“…However, due to their increasing availability, non-invasive, high-speed and accurate nature, optoelectronic infra-red measurement devices have now become a standard technique for the capture of human movement. In order to improve the robustness of MS simulations (De Groote et al 2010;Lu et al 1997), specific approaches for the reduction of soft tissue artefact (Taylor et al 2005) and the assessment of the underlying robustness of MS simulation in OpenSim using different scaling methods and differently weighted kinematic concepts, as well as to estimate the resulting errors in terms of kinematics and kinetics.…”

Section: Introductionmentioning

confidence: 99%

Robustness of kinematic weighting and scaling concepts for musculoskeletal simulation

Schellenberg

Taylor

Jonkers

et al. 2017

Computer Methods in Biomechanics and Biomedical Engineering

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Musculoskeletal modelling is widely used to estimate internal loading conditions. In order to optimise robustness and reduce errors between the subject-specific reference motion data (RMD) and the musculoskeletal simulation, 90 permutations of kinetic and kinematic data were analysed during split squats. A ranking for the scaling and kinematic weighting concepts based on the RMS errors when including functional centres of rotation (fCoRs), joint angles, and skin markers, revealed that analyses should include fCoR in the scaling and the simulation processes, as well as an automated weighting procedure including all attached skin markers for optimal registration of the musculoskeletal model to the RMD.

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Sensitivity of dynamic simulations of gait and dynamometer experiments to hill muscle model parameters of knee flexors and extensors

Cited by 96 publications

References 22 publications

Sensitivity of model predictions of muscle function to changes in moment arms and muscle–tendon properties: A Monte-Carlo analysis

Sensitivity of model predictions of muscle function to changes in moment arms and muscle–tendon properties: A Monte-Carlo analysis

Is My Model Good Enough? Best Practices for Verification and Validation of Musculoskeletal Models and Simulations of Movement

Robustness of kinematic weighting and scaling concepts for musculoskeletal simulation

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