On validation of multibody musculoskeletal models

Lund, Morten Enemark; Zee, Mark de; Andersen, Michael Skipper; Rasmussen, John

doi:10.1177/0954411911431516

Cited by 110 publications

(90 citation statements)

References 59 publications

(88 reference statements)

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“…Most biomechanics researchers are aware of the importance of validation, but the field lacks best practices for the challenging process of verifying and validating NMS models and simulations. Several papers [3][4][5] have laid the groundwork, identifying principles and considerations, but these papers stop short of providing specific guidelines for NMS modeling and simulation. The knowledge and practices of how to best validate a biomechanical model and verify modeling software used in past research studies have not been adequately synthesized.…”

Section: Introductionmentioning

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

510

390

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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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Section: Introductionmentioning

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

510

390

View full text Add to dashboard Cite

show abstract

“…Despite the potential, and the growing expectations of what computer models can achieve, clinical adoption of musculoskeletal models has been slow. Many barriers remain before models can achieve their full potential and, in recent years, increasing emphasis has been placed on the fidelity of musculoskeletal models and the assumptions upon which models rely (Lund et al 2012).…”

Section: Introductionmentioning

confidence: 99%

“…The dependency on the operator is especially relevant in the context of general musculoskeletal modelling packages such as LifeMOD (Lifemodeler Inc 2010), SIMM (Musculographics Inc 2013), AnyBody (Damsgaard et al 2006) and OpenSim (Delp et al 2007). Such modelling systems and their associated models have a host of parameters that require manual calibration before they can be applied to specific subjects or in specific applications (Lund et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Scaling of musculoskeletal models from static and dynamic trials

Lund

Andersen

Zee

et al. 2015

International Biomechanics

Self Cite

121

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Subject-specific scaling of cadaver-based musculoskeletal models is important for accurate musculoskeletal analysis within multiple areas such as ergonomics, orthopaedics and occupational health. We present two procedures to scale 'generic' musculoskeletal models to match segment lengths and joint parameters to a specific subject and compare the results to a simpler approach based on linear, segment-wise scaling. By incorporating data from functional and standing reference trials, the new scaling approaches reduce the model sensitivity to assumed model marker positions. For validation, we applied all three scaling methods to an inverse dynamics-based musculoskeletal model and compared predicted knee joint contact forces to those measured with an instrumented prosthesis during gait. Additionally, a Monte Carlo study was used to investigate the sensitivity of the knee joint contact force to random adjustments of the assumed model marker positions (+/− one marker diameter). The model based on linear scaling showed the highest variation in the knee joint contact force of 1.44 body weight (BW) around contra-lateral heel strike, and a variation in root mean square deviation (RMSD) of 0.36 BW. The proposed methods reduced the variation to 1.0 BW (RMSD 0.26 BW) for the anatomical landmark based method and 0.47 BW (RMSD 0.06 BW) for the functional based method. Variation in model predictions due to uncertainty in marker positions is a trait of all marker-based musculoskeletal modelling approaches. The presented methods solve part of this problem and rely less on manual identification of anatomical landmarks in the model. The work represents a step towards a more consistent methodology in musculoskeletal modelling.

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“…Even though the contribution of the GH joint capsule and ligaments is expected to be small in the range of motion analysed in this study [39,45] , their preclusion from the biomechanical model developed may limit its predictive capacity at extreme ranges of motion. The GH joint translations estimated in this study are considered valid based on their consistency with previous reports, but further experiments are still required to validate the musculoskeletal model developed [50] . Finally, the spherical shape of the GH joint can be questioned according to the observations of Lopes et al [28] , being additional research needed to assess its influence on the anatomical description of the joint.…”

Section: Discussionmentioning

confidence: 90%