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ABSTRACT: Older adults walk more slowly, take shorter steps, and spend more time with both legs on the ground compared to young adults. Although many studies have investigated the effects of aging on the kinematics and kinetics of gait, little is known about the corresponding changes in muscle function. The aim of this study was to describe and compare the actions of the lower-limb muscles in accelerating the body's center of mass (COM) in healthy young and older adults. Three-dimensional gait analysis and subject-specific musculoskeletal modeling were used to calculate lower-limb muscle forces and muscle contributions to COM accelerations when both groups walked at the same speed. The orientations of all body segments during walking, except that of the pelvis, were invariant to age when these quantities were expressed in a global reference frame. The older subjects tilted their pelves more anteriorly during the stance phase. The mean contributions of the gluteus maximus, gluteus medius, vasti, gastrocnemius and soleus to the vertical, fore-aft and mediolateral COM accelerations (support, progression and balance, respectively) were similar in the two groups. However, the gluteus medius contributed significantly less to support (p<0.05) while the gluteus maximus and contralateral erector spinae contributed significantly more to balance (p<0.05) during early stance in the older subjects. These results provide insight into the functional roles of the individual leg muscles during gait in older adults, and highlight the importance of the hip and back muscles in controlling mediolateral balance.
Gait & posture 12/2012; · 2.58 Impact Factor
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ABSTRACT: Hill-type muscle models are commonly used in musculoskeletal models to estimate muscle forces during human movement. However, the sensitivity of model predictions of muscle function to changes in muscle moment arms and muscle-tendon properties is not well understood. In the present study, a three-dimensional muscle-actuated model of the body was used to evaluate the sensitivity of the function of the major lower limb muscles in accelerating the whole-body center of mass during gait. Monte-Carlo analyses were used to quantify the effects of entire distributions of perturbations in the moment arms and architectural properties of muscles. In most cases, varying the moment arm and architectural properties of a muscle affected the torque generated by that muscle about the joint(s) it spanned as well as the torques generated by adjacent muscles. Muscle function was most sensitive to changes in tendon slack length and least sensitive to changes in muscle moment arm. However, the sensitivity of muscle function to changes in moment arms and architectural properties was highly muscle-specific; muscle function was most sensitive in the cases of gastrocnemius and rectus femoris and insensitive in the cases of hamstrings and the medial sub-region of gluteus maximus. The sensitivity of a muscle's function was influenced by the magnitude of the muscle's force as well as the operating region of the muscle on its force-length curve. These findings have implications for the development of subject-specific models of the human musculoskeletal system.
Journal of biomechanics 04/2012; 45(8):1463-71. · 2.66 Impact Factor
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ABSTRACT: The aims of this study were to evaluate and explain the individual muscle contributions to the medial and lateral knee compartment forces during gait, and to determine whether these quantities could be inferred from their contributions to the external knee adduction moment. Gait data from eight healthy male subjects were used to compute each individual muscle contribution to the external knee adduction moment, the net tibiofemoral joint reaction force, and reaction moment. The individual muscle contributions to the medial and lateral compartment forces were then found using a least-squares approach. While knee-spanning muscles were the primary contributors, non-knee-spanning muscles (e.g., the gluteus medius) also contributed substantially to the medial compartment compressive force. Furthermore, knee-spanning muscles tended to compress both compartments, while most non-knee-spanning muscles tended to compress the medial compartment but unload the lateral compartment. Muscle contributions to the external knee adduction moment, particularly those from knee-spanning muscles, did not accurately reflect their tendencies to compress or unload the medial compartment. This finding may further explain why gait modifications may reduce the knee adduction moment without necessarily decreasing the medial compartment force.
Journal of Orthopaedic Research 03/2012; 30(10):1586-95. · 2.81 Impact Factor
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ABSTRACT: The aim of this study was to compare muscle-force estimates derived for human locomotion using three different methods commonly reported in the literature: static optimisation (SO), computed muscle control (CMC) and neuromusculoskeletal tracking (NMT). In contrast with SO, CMC and NMT calculate muscle forces dynamically by including muscle activation dynamics. Furthermore, NMT utilises a time-dependent performance criterion, wherein a single optimisation problem is solved over the entire time interval of the task. Each of these methods was used in conjunction with musculoskeletal modelling and experimental gait data to determine lower-limb muscle forces for self-selected speeds of walking and running. Correlation analyses were performed for each muscle to quantify differences between the various muscle-force solutions. The patterns of muscle loading predicted by the three methods were similar for both walking and running. The correlation coefficient between any two sets of muscle-force solutions ranged from 0.46 to 0.99 (p < 0.001 for all muscles). These results suggest that the robustness and efficiency of static optimisation make it the most attractive method for estimating muscle forces in human locomotion.
Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine 02/2012; 226(2):103-12. · 1.21 Impact Factor
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ABSTRACT: Computational analyses of leg-muscle function in human locomotion commonly assume that contact between the foot and the ground occurs at discrete points on the sole of the foot. Kinematic constraints acting at these contact points restrict the motion of the foot and, therefore, alter model calculations of muscle function. The aim of this study was to evaluate how predictions of muscle function obtained from musculoskeletal models are influenced by the model used to simulate ground contact. Both single- and multiple-point contact models were evaluated. Muscle function during walking and running was determined by quantifying the contributions of individual muscles to the vertical, fore-aft and mediolateral components of the ground reaction force (GRF). The results showed that two factors--the number of foot-ground contact points assumed in the model and the type of kinematic constraint enforced at each point--affect the model predictions of muscle coordination. Whereas single- and multiple-point contact models produced similar predictions of muscle function in the sagittal plane, inconsistent results were obtained in the mediolateral direction. Kinematic constraints applied in the sagittal plane altered the model predictions of muscle contributions to the vertical and fore-aft GRFs, while constraints applied in the frontal plane altered the calculations of muscle contributions to the mediolateral GRF. The results illustrate the sensitivity of calculations of muscle coordination to the model used to simulate foot-ground contact.
Computer Methods in Biomechanics and Biomedical Engineering 05/2011; 15(6):657-68. · 0.85 Impact Factor
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ABSTRACT: When a muscle is activated, it contracts, develops force, and, by virtue of a lever arm, exerts a torque about one or more of the joints it spans. Dynamic coupling in a multi-joint system ensures that a muscle can simultaneously accelerate all the joints in the body, even those it does not span. The overall goal of the present study was to develop an accurate and computationally efficient method for quantifying muscle function during human locomotion. Our specific aims were first to assess the accuracy of the method by comparing its results against reference data reported in the literature; and second to demonstrate the application of the method in the study of leg-muscle function in walking and running. The method is based on using a pseudo-inverse to decompose the ground reaction force, enabling each muscle's contribution to the acceleration of any point on the body to be found at each instant of the gait cycle. One of the most appealing features of the pseudo-inverse method is that it can be applied either to data generated from a computer simulation or to measurements obtained from a gait experiment. The pseudo-inverse method is also roughly three orders of magnitude faster than an alternative approach based on perturbation analysis, which is commonly used to assess muscle function in human gait. The results show that the pseudo-inverse method is able to accurately reproduce reference data reported for normal walking. Predictions of leg-muscle function in walking and running are also consistent with findings reported previously by others. Copyright © 2010 John Wiley & Sons, Ltd.
International Journal for Numerical Methods in Biomedical Engineering. 06/2010; 27(3):436 - 449.
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ABSTRACT: The aim of this study was to describe and explain how individual muscles control mediolateral balance during normal walking. Biomechanical modeling and experimental gait data were used to quantify individual muscle contributions to the mediolateral acceleration of the center of mass during the stance phase. We tested the hypothesis that the hip, knee, and ankle extensors, which act primarily in the sagittal plane and contribute significantly to vertical support and forward progression, also accelerate the center of mass in the mediolateral direction. Kinematic, force plate, and muscle EMG data were recorded simultaneously for five healthy subjects who walked at their preferred speeds. The body was modeled as a 10-segment, 23 degree-of-freedom skeleton, actuated by 54 muscles. Joint moments obtained from inverse dynamics were decomposed into muscle forces by solving an optimization problem that minimized the sum of the squares of the muscle activations. Muscles contributed significantly to the mediolateral acceleration of the center of mass throughout stance. Muscles that generated both support and forward progression (vasti, soleus, and gastrocnemius) also accelerated the center of mass laterally, in concert with the hip adductors and the plantarflexor everters. Gravity accelerated the center of mass laterally for most of the stance phase. The hip abductors, anterior and posterior gluteus medius, and, to a much lesser extent, the plantarflexor inverters, actively controlled balance by accelerating the center of mass medially.
Journal of biomechanics 05/2010; 43(11):2055-64. · 2.66 Impact Factor
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ABSTRACT: Contact occurs in a wide variety of multibody dynamic systems, including the human musculoskeletal system. However, sensitivity and optimization studies of such systems have been limited by the high computational cost of repeated contact analyses. This study presents a novel surrogate modeling approach for performing computationally efficient three-dimensional elastic contact analyses within multibody dynamic simulations. The approach fits a computationally cheap surrogate contact model to data points sampled from a computationally expensive elastic contact model (e.g., a finite element or elastic foundation model) and resolves several unique challenges involved in applying surrogate modeling techniques to elastic contact problems. As an example application, we performed multibody dynamic simulations of a Stanmore wear simulator machine using surrogate and elastic foundation (EF) contact models of a total knee replacement. Accuracy was assessed by performing eleven dynamic simulations with both types of contact models utilizing large variations in motion and load inputs to the machine. Wear volumes predicted with the surrogate contact models were within 1.5% of those predicted with the EF contact models. Computational speed was assessed by performing five Monte Carlo analyses (over 1000 dynamic simulations each) with surrogate contact models utilizing realistic variations in motion and load inputs. Computation time was reduced from an estimated 284 h per analysis with the EF contact models to 1.4 h with the surrogate contact models (i.e., 17 min vs. 5 s per simulation), with higher wear sensitivity observed for motion variations than for load variations. The proposed surrogate modeling approach can significantly improve the computational speed of multibody dynamic simulations incorporating three-dimensional elastic contact models with general surface geometry.
Medical Engineering & Physics 03/2010; 32(6):584-94. · 1.62 Impact Factor
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ABSTRACT: Musculoskeletal models are currently the primary means for estimating in vivo muscle and contact forces in the knee during gait. These models typically couple a dynamic skeletal model with individual muscle models but rarely include articular contact models due to their high computational cost. This study evaluates a novel method for predicting muscle and contact forces simultaneously in the knee during gait. The method utilizes a 12 degree-of-freedom knee model (femur, tibia, and patella) combining muscle, articular contact, and dynamic skeletal models. Eight static optimization problems were formulated using two cost functions (one based on muscle activations and one based on contact forces) and four constraints sets (each composed of different combinations of inverse dynamic loads). The estimated muscle and contact forces were evaluated using in vivo tibial contact force data collected from a patient with a force-measuring knee implant. When the eight optimization problems were solved with added constraints to match the in vivo contact force measurements, root-mean-square errors in predicted contact forces were less than 10 N. Furthermore, muscle and patellar contact forces predicted by the two cost functions became more similar as more inverse dynamic loads were used as constraints. When the contact force constraints were removed, estimated medial contact forces were similar and lateral contact forces lower in magnitude compared to measured contact forces, with estimated muscle forces being sensitive and estimated patellar contact forces relatively insensitive to the choice of cost function and constraint set. These results suggest that optimization problem formulation coupled with knee model complexity can significantly affect predicted muscle and contact forces in the knee during gait. Further research using a complete lower limb model is needed to assess the importance of this finding to the muscle and contact force estimation process.
Journal of biomechanics 12/2009; 43(5):945-52. · 2.66 Impact Factor
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ABSTRACT: Computational speed is a major limiting factor for performing design sensitivity and optimization studies of total knee replacements. Much of this limitation arises from extensive geometry calculations required by contact analyses. This study presents a novel surrogate contact modeling approach to address this limitation. The approach involves fitting contact forces from a computationally expensive contact model (e.g., a finite element model) as a function of the relative pose between the contacting bodies. Because contact forces are much more sensitive to displacements in some directions than others, standard surrogate sampling and modeling techniques do not work well, necessitating the development of special techniques for contact problems. We present a computational evaluation and practical application of the approach using dynamic wear simulation of a total knee replacement constrained to planar motion in a Stanmore machine. The sample points needed for surrogate model fitting were generated by an elastic foundation (EF) contact model. For the computational evaluation, we performed nine different dynamic wear simulations with both the surrogate contact model and the EF contact model. In all cases, the surrogate contact model accurately reproduced the contact force, motion, and wear volume results from the EF model, with computation time being reduced from 13 min to 13 s. For the practical application, we performed a series of Monte Carlo analyses to determine the sensitivity of predicted wear volume to Stanmore machine setup issues. Wear volume was highly sensitive to small variations in motion and load inputs, especially femoral flexion angle, but not to small variations in component placements. Computational speed was reduced from an estimated 230 h to 4 h per analysis. Surrogate contact modeling can significantly improve the computational speed of dynamic contact and wear simulations of total knee replacements and is appropriate for use in design sensitivity and optimization studies.
Journal of Biomechanical Engineering 05/2009; 131(4):041010. · 1.90 Impact Factor
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ABSTRACT: When optimization is used to evaluate a joint contact model's ability to reproduce experimental measurements, the high computational cost of repeated contact analysis can be a limiting factor. This paper presents a computationally-efficient response surface optimization methodology to address this limitation. Quadratic response surfaces were fit to contact quantities (contact force, maximum pressure, average pressure, and contact area) predicted by a discrete element contact model of the tibiofemoral joint for various combinations of material modulus and relative bone pose (i.e., position and orientation). The response surfaces were then used as surrogates for costly contact analyses in optimizations that minimized differences between measured and predicted contact quantities. The methodology was evaluated theoretically using six sets of synthetic (i.e., computer-generated) contact data, and practically using one set of experimental contact data. For the synthetic cases, the response surface optimizations recovered all contact quantities to within 3.4% error. For the experimental case, they matched all contact quantities to within 6.3% error except for maximum contact pressure, which was in error by up to 50%. Response surface optimization provides rapid evaluation of joint contact models within a limited range of relative bone poses and can help identify potential weaknesses in contact model formulation and/or experimental data quality.
Journal of applied biomechanics 06/2006; 22(2):120-30. · 0.76 Impact Factor
