Elsevier

Journal of Biomechanics

Volume 44, Issue 11, 28 July 2011, Pages 2096-2105
Journal of Biomechanics

Accuracy of generic musculoskeletal models in predicting the functional roles of muscles in human gait

https://doi.org/10.1016/j.jbiomech.2011.05.023Get rights and content

Abstract

Biomechanical assessments of muscle function are often performed using a generic musculoskeletal model created from anatomical measurements obtained from cadavers. Understanding the validity of using generic models to study movement biomechanics is critical, especially when such models are applied to analyze the walking patterns of persons with impaired mobility. The aim of this study was to evaluate the accuracy of scaled-generic models in determining the moment arms and functional roles of the lower-limb muscles during gait. The functional role of a muscle was described by its potential to contribute to the acceleration of a joint or the acceleration of the whole-body center of mass. A muscle’s potential acceleration was defined as the acceleration induced by a unit of muscle force. Dynamic simulations of walking were generated for four children with cerebral palsy and five age-matched controls. Each subject was represented by a scaled-generic model and a model developed from magnetic resonance (MR) imaging. Calculations obtained from the scaled-generic model of each subject were evaluated against those derived from the corresponding MR-based model. Substantial differences were found in the muscle moment arms computed using the two models. These differences propagated to calculations of muscle potential accelerations, but predictions of muscle function (i.e., the direction in which a muscle accelerated a joint or the center of mass and the magnitude of the muscle’s potential acceleration relative to that of other muscles) were consistent between the two modeling techniques. Our findings suggest that scaled-generic models and image-based models yield similar assessments of muscle function in both normal and pathological gait.

Introduction

Biomechanical assessments of muscle function are often performed using generic models of the body. Generic musculoskeletal models (e.g., Amis et al., 1979, Delp et al., 1990, Holzbaur et al., 2005, Klein Horsman et al., 2007, Ward et al., 2009) are typically based on measurements obtained from cadaveric specimens or medical images, and are presumed to be representative of able-bodied adults. Scaled-generic models are created by scaling body-segment anthropometry, joint geometry, and muscle-tendon attachment sites in the model to corresponding parameters in individual subjects. Such scaling requires measurements of body mass and limb lengths, the latter often estimated from surface-mounted markers or from direct measurements of distances between anatomical landmarks. The accuracy of scaled-generic models in representing musculoskeletal anatomy remains largely untested, mainly because of the time-intensive nature of creating benchmark models from subject-specific medical images. Scheys et al., 2008a, Scheys et al., 2008b assessed the mechanical advantage (i.e., moment arms) of individual muscles computed from scaled-generic and image-based models, and found significant errors due to inter-subject anatomical variability and bony abnormalities. However, no study to our knowledge has determined the effects of scaling on muscle function during any task. Understanding the validity of using scaled-generic models to study movement biomechanics is critical, especially for patients with conditions such as cerebral palsy (CP), where bone and muscle abnormalities are the rule rather than the exception.

Magnetic resonance (MR) scanners provide high-resolution images that allow visualization of anatomical structures in vivo. From these images, geometrical model parameters can be found to describe joint locations and orientations, muscle attachment sites, and bony torsion (Scheys et al., 2006, Blemker et al., 2007). MR-based models are able to reproduce bone geometry accurately (Smith et al., 1989) and have been shown to closely approximate muscle moment-arm measurements obtained from cadaver specimens (Murray et al., 1998, Arnold et al., 2000). Although processing algorithms can expedite extraction of bone structures (Hoad and Martel, 2002, Zoroofi et al., 2004) and muscle paths (Scheys et al., 2009), image-based models remain prohibitively expensive to implement in clinical environments due to MR scan costs and image processing time (Viceconti et al., 2006, Blemker et al., 2007).

Musculoskeletal computer models and simulations yield information that cannot be obtained non-invasively by direct measurement. Muscle forces can be determined using inverse-dynamics-based optimization, dynamic optimization, and EMG-driven modeling (Zajac, 1993, Lloyd and Besier, 2003, Erdemir et al., 2007, Pandy and Andriacchi, 2010); and neuromuscular coordination patterns can be revealed through muscle contributions to joint accelerations and the acceleration of the body’s center of mass (Anderson and Pandy, 2003, Neptune et al., 2004, Liu et al., 2006, Pandy et al., 2010). Unfortunately, optimization models cannot readily be applied to study the gait patterns of individuals with neurological conditions such as CP, which are characterized by sub-optimal neural function. The EMG-driven modeling approach also has limitations, principal among these being difficulties associated with normalization of the EMG data and non-invasive measurement of deep-lying muscle activity.

Geometric assessments of muscle function are not affected by the aforementioned limitations. The functional role of a given muscle can be quantified by a potential contribution to the acceleration of a joint or the center of mass, defined as the acceleration that a unit muscle force (1 N) would generate (Schwartz and Lakin, 2003, Arnold et al., 2005, Hicks et al., 2007, Hicks et al., 2008). This quantity provides insight into how the relative positions of muscles, bones, and joints influence a muscle’s capacity to generate motion. Among the most important accelerations in walking are the vertical and fore-aft accelerations of the center of mass (representing support and forward progression, respectively), and the flexion–extension accelerations of the ipsilateral hip, knee, and ankle joints (Anderson and Pandy, 2003, Arnold et al., 2005, Liu et al., 2006).

The overall goal of this study was to evaluate the accuracy of scaled-generic musculoskeletal models relative to MR-based models in determining the potential contributions of the lower-limb muscles to joint accelerations and the acceleration of the body’s center of mass during walking. This evaluation was performed for a cohort of children with CP and a cohort of age-matched controls. Our specific aims were firstly, to evaluate errors in muscle moment-arm calculations produced by scaled-generic models; and secondly, to explore whether these errors propagate to patterns of muscle function predicted by scaled-generic models.

Section snippets

Methods

Scaled-generic and MR-based musculoskeletal models were developed for nine study participants. Four subjects (2 males and 2 females; age, 10.5±3.7 years; height, 133.2±14.8 cm; weight, 30.6±4.7 kg) diagnosed with spastic diplegic CP were identified from medical records held at the Royal Children’s Hospital in Melbourne. The other five subjects (3 males and 2 females; age, 10.1±2.0 years; height, 141.1±12.6 cm; weight 40.6±12.2 kg) were able-bodied controls. Subjects were aged between 7 and 15

Results

The average walking speeds of the control subjects and CP patients were 1.3±0.2 m/s and 1.0±0.1 m/s, respectively. The simulation results displayed consistency with the experimental data and with the results of previous studies. Simulated joint angles of the pelvis, hips, knees, and ankles were within 4° of the experimental data for all subjects. Sagittal-plane joint angles displayed by the CP patients differed from the mean data obtained for the control subjects by up to 20° (Fig. 1). The

Discussion

To our knowledge, this is the first study to examine the accuracy of scaled-generic models in assessing the functional roles of muscles during gait. Our aim was to evaluate the accuracy of scaled-generic models in computing both muscle moment arms and the potential contributions of individual muscles to joint and center-of-mass accelerations in normal and pathological gait. The accuracy of these models was quantified by comparing their predictions against those obtained from MR-based models,

Conflict of interest statement

The authors do not have any financial or personal relationships with other people or organizations that could inappropriately influence their work.

Acknowledgments

We thank Tim Dorn for his gait data extraction software, Morgan Sangeux for his MATLAB-OpenSim interface code, Pam Thomason, for assisting with subject recruitment and gait analyses, and Jana Bechmann for MR-based model development. Financial support was provided by the Australian Research Council under Discovery Project Grant DP0878705, the National Health and Medical Research Council through the Centre for Clinical Research Excellence in Gait Analysis and Gait Rehabilitation, and a VESKI

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