Personalized MR-based musculoskeletal models compared to rescaled generic models in the presence of increased femoral anteversion: Effect on hip moment arm lengths

Scheys, Lennart; Campenhout, Anja Van; Spaepen, Arthur; Suetens, Paul; Jonkers, Ilse

doi:10.1016/j.gaitpost.2008.05.002

Cited by 109 publications

(88 citation statements)

References 17 publications

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“…The range of the muscle lever arm (cm). muscle bundles the model Scheys et al [37] Arnold et al [38] Bonnefoy et al [39] White et al [40] Kepple et al [41] Pierrynowski et al [42,43] hip abduction moment arm gluteus medius anterior EMG-driven muscle forces was R 2 ¼ 0.38, and the average error was RMSE ¼ 61 N. Physiologically possible muscle synergies comprised muscle forces ranging from zero to the peak muscle force for most muscles. Twenty-seven out of the 34 lower-limb muscles ranged from zero to their peak force whereas seven muscles (gluteus maximus, adductor magnus, semimembranosus, vastus medialis, vastus lateralis, vastus medialis and soleus) could not reach their peak force.…”

Section: Resultsmentioning

confidence: 99%

Stochastic modelling of muscle recruitment during activity

et al. 2015

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One contribution of 11 to a theme issue 'Multiscale modelling in biomechanics: theoretical, computational and translational challenges'. Muscle forces can be selected from a space of muscle recruitment strategies that produce stable motion and variable muscle and joint forces. However, current optimization methods provide only a single muscle recruitment strategy. We modelled the spectrum of muscle recruitment strategies while walking. The equilibrium equations at the joints, muscle constraints, static optimization solutions and 15-channel electromyography (EMG) recordings for seven walking cycles were taken from earlier studies. The spectrum of muscle forces was calculated using Bayesian statistics and Markov chain Monte Carlo (MCMC) methods, whereas EMG-driven muscle forces were calculated using EMG-driven modelling. We calculated the differences between the spectrum and EMG-driven muscle force for 1-15 input EMGs, and we identified the muscle strategy that best matched the recorded EMG pattern. The best-fit strategy, static optimization solution and EMG-driven force data were compared using correlation analysis. Possible and plausible muscle forces were defined as within physiological boundaries and within EMG boundaries. Possible muscle and joint forces were calculated by constraining the muscle forces between zero and the peak muscle force. Plausible muscle forces were constrained within six selected EMG boundaries. The spectrum to EMG-driven force difference increased from 40 to 108 N for 1-15 EMG inputs. The best-fit muscle strategy better described the EMG-driven pattern (R 2 ¼ 0.94; RMSE ¼ 19 N) than the static optimization solution (R 2 ¼ 0.38; RMSE ¼ 61 N). Possible forces for 27 of 34 muscles varied between zero and the peak muscle force, inducing a peak hip force of 11.3 body-weights. Plausible muscle forces closely matched the selected EMG patterns; no effect of the EMG constraint was observed on the remaining muscle force ranges. The model can be used to study alternative muscle recruitment strategies in both physiological and pathophysiological neuromotor conditions.

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Section: Resultsmentioning

confidence: 99%

Stochastic modelling of muscle recruitment during activity

et al. 2015

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“…[64][65][66]) or can be estimated by segmenting MR images (e.g., Refs. [63] and [67]). Origin and insertion points are typically defined as the centroids of the muscle attachments in the cadaver or imaged subject, but via points and wrapping surfaces have been defined several ways.…”

Section: Modeling Choicesmentioning

confidence: 98%

“…Duda et al [82] digitized femoral muscle attachment sites for six cadavers and found that the SD of centroid locations was 80% of the mean. Attachments can also vary with pathology, such as bone deformities common in children with cerebral palsy [67].…”

Section: Verifying Musculoskeletal Geometrymentioning

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

522

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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“…Con el fin de desarrollar modelos que permitan analizar diferentes relaciones particulares, como por ejemplo la localización precisa de los centros de giro articulares, Scheys (52,66) ajustó las condiciones iniciales y el dominio geométrico del modelo músculo-esquelético computacional de Delp (45,62), usando las resonancias magnéticas de los pacientes. Sin embargo, esta técnica es costosa y dependiente de un procesamiento que filtre adecuadamente el ruido asociado con la captura (52,66).…”

Section: Figura 6 La Figura Muestra La Simulación Del Modelo Computaunclassified

“…Sin embargo, esta técnica es costosa y dependiente de un procesamiento que filtre adecuadamente el ruido asociado con la captura (52,66).…”

Section: Figura 6 La Figura Muestra La Simulación Del Modelo Computaunclassified

Análisis teórico y computacional de la marcha normal y patológica: una revisión

2010

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ResumenLa marcha humana es el resultado de la compleja interacción entre varios subsistemas: neuromuscular, músculo-tendinoso y osteoarticular, que trabajan coordinadamente para generan la dinámica corporal necesaria para el desplazamiento bípedo. En la rutina clínica, el estudio de la marcha es la base de la identificación de trastornos patológicos, facilitando su diagnóstico, tratamiento y seguimiento. Tradicionalmente este análisis determina el conjunto de patrones que describen la dinámica del sistema. Sin embargo, éste análisis es insuficiente para evaluar algunos movimientos, sobre todo para los estadios tempranos de casi todos los movimientos patológicos. El desarrollo de diferentes modelos normales y patológicos ha permitido establecer diferencias objetivas para cada una de estas situaciones. En este artículo se hace una revisión de los modelos que describen la dinámica de la marcha humana normal y patológica, inspirados en la morfo-fisiología del sistema locomotor. Además, se hace un análisis sobre la efectividad de los modelos propuestos en la literatura para describir comportamientos patológicos.Palabras clave: modelos teóricos, marcha, biomecánica, ingeniería biomédica THEORETICAL AND COMPUTATIONAL ANALYSIS OF NORMAL AND PATHOLOGICAL GAIT: A REVIEW AbstractThe human gait is the result of complex interactions between several sub-systems: neuromuscular, musculo-tendinous and osteo-articular, which work together to generate the body dynamics necessary to describe the bipedal movement. In the clinical routine, the gait analysis is the main element for identifying pathological disorders, supporting the diagnosis and facilitating a proper follow up. Traditionally, this analysis aims to establish the set of patterns that describe the dynamics of the system. However, this analysis is insufficient for some movements, especially for early stages of almost every pathological movement. The development of normal and pathological models has allowed to demostrate objective differences for each of these situations. In this article we present a summary of the models that describe the dynamics of the normal and pathological human gait, inspired by the morpho-physiology of the locomotor system. Furthermore, we perform an analysis of the effectiveness of the proposed models in the literature.

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Personalized MR-based musculoskeletal models compared to rescaled generic models in the presence of increased femoral anteversion: Effect on hip moment arm lengths

Cited by 109 publications

References 17 publications

Stochastic modelling of muscle recruitment during activity

Stochastic modelling of muscle recruitment during activity

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

Análisis teórico y computacional de la marcha normal y patológica: una revisión

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