Muscle Contributions to Propulsion and Support During Running

Hamner, Samuel R.; John, Chand T.; Higginson, Jill S.; Delp, Scott L.

doi:10.1249/01.mss.0000352942.73148.78

Cited by 111 publications

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“…A possible reason could be the low muscle mass in this model. The muscle mass was determined from the maximum isometric force, the total of which was a factor three lower than in a three dimensional model [38]. Model UMBE03 underestimated the increase in metabolic cost from level to uphill, and the decrease in metabolic cost from level to downhill.…”

Section: Discussionmentioning

confidence: 99%

Metabolic cost calculations of gait using musculoskeletal energy models, a comparison study

Koelewijn

Heinrich

Bogert

2019

Preprint

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13This paper compares predictions of metabolic energy expenditure in gait using 14 seven metabolic energy expenditure models to assess their correlation with 15 experimental data. Ground reaction forces, marker data, and pulmonary gas 16 exchange data were recorded for six walking trials at combinations of two speeds, 17 0.8 m/s and 1.3 m/s, and three inclines, -8% (downhill), level, and 8% (uphill). 18 The metabolic cost, calculated with the metabolic energy models was compared to 19 the metabolic cost from the pulmonary gas exchange rates. A repeated measures 20 correlation showed that the model by Bhargava et al. [7] and the model by 21 Lichtwark and Wilson [21] correlated best with the experimental data. It was 22 found that for all metabolic energy models, the subject's weight affected the 23 calculated metabolic cost. 24 Introduction 25 Humans prefer to walk in energetically optimal ways. Walking speed [1], the ratio 26 between step length and frequency [2], step width [3] and vertical movement of the 27 March 25, 2019 1/25 center of mass [4, 5] are chosen to minimize energy expenditure. Whole-body energy 28 expenditure can be measured using direct calorimetry, by measuring the heat 29 production in the body, or indirect calorimetry, by measuring the volume of oxygen 30 inspired and carbon dioxide expired [6]. However, these measurements are not always 31 available, but an accurate prediction of energy expenditure is often desired. 32 Instead, musculoskeletal modeling can be used to simulate human gait and to 33 calculate the energy expenditure based on a metabolic energy expenditure model (e.g., 34 [7-10]). These calculations are based on variables that are studied in gait analysis, such 35 as joint moments, joint power, or muscle forces, lengths and activations [11]. With a 36 metabolic energy expenditure model, the energy expenditure can be calculated for gait 37 experiments that did not take metabolic energy expenditure measurements, or for 38 predictive gait simulations, where no experimental data is available. Recently, these 39 simulations have been used to analyze 'what-if scenarios' such as the effect of an 40 intervention such as a prosthesis [12], an exoskeleton [13], ankle foot orthosis [14], 41 additional weight [15], loaded and inclined walking [16], or changing the gait pattern to 42 minimize knee reaction force [17] on gait. Energy cost is an important variable in gait, 43 and should be considered whenever a clinical intervention is studied or designed. 44 In literature several energy models were suggested. The Huxley crossbridge 45 model [18] finds both the muscle force and the energy expenditure of a muscle, but 46 requires up to 18 states [19]. Instead, Hill-type muscle models [20] are typically used to 47 simulate muscles, but these do not output metabolic energy expenditure. Therefore, 48 several metabolic energy expenditure models have been proposed that calculate the 49 energy expenditure during walking based on Hill-type muscles [7-10, 21, 22], based on 50 muscle effici...

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Section: Discussionmentioning

confidence: 99%

Metabolic cost calculations of gait using musculoskeletal energy models, a comparison study

Koelewijn

Heinrich

Bogert

2019

Preprint

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“…Adductor magnus is the largest of the hip adductor muscle group, occupying 63% of their combined volume (Takizawa et al 2014b), and has the second largest cross-sectional area of all muscles in the lower limb (Ito et al 2003). It is understood to play an important role in stabilizing the hip joint (Green and Morris, 1970;Takizawa et al 2014b), as well as contributing to the hip joint's tri-planar range of motion during functional tasks such as walking (Green and Morris, 1970;Gazendam and Hof, 2007;Kolk et al 2015) and running (Gazendam and Hof, 2007;Hamner et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Adductor magnus: An EMG investigation into proximal and distal portions and direction specific action

et al. 2018

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Cadaveric studies indicate that adductor magnus is structurally partitioned into at least two regions. The aim of this study was to investigate the direction-specific actions of proximal and distal portions of adductor magnus, and in doing so determine if these segments have distinct functional roles. Fine-wire EMG electrodes were inserted into two portions of adductor magnus of 12 healthy young adults. Muscle activity was recorded during maximum voluntary isometric contractions (MVICs) across eight tests (hip flexion/extension, internal/external rotation, abduction, and adduction at 0°, 45°, and 90° hip flexion). Median activity within each action (normalized to peak) was compared between segments using repeated measures nonparametric tests (α = 0.05). An effect size (ES = z-score/√sample size) was calculated to determine the magnitude of difference between muscle segments. The relative contribution of each muscle segment differed significantly during internal rotation (P < 0.001; ES = 0.88) and external rotation (P = 0.003, ES = 0.79). The distal portion was most active during extension [median (interquartile range); 100(0)% MVIC)] and internal rotation [58(34)% MVIC]. The proximal portion was most active during extension [100(49)% MVIC] and adduction [59(64)%MVIC], with low level activity during external rotation [15(41)%MVIC]. This study suggests that adductor magnus has at least two functionally unique regions. Differences were most evident during rotation. The different direction-specific actions may imply that each segment performs separate roles in hip stability and movement. These findings may have implications on injury prevention and rehabilitation for adductor-related groin injuries, hamstring strain injury, and hip pathology. Clin. Anat. 31:535-543, 2018. © 2018 Wiley Periodicals, Inc.

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“…In the third example, we performed tracking simulations of walking with a three-dimensional (3D) musculoskeletal model (29 DOFs, 92 muscles actuating the lower limbs and trunk, 8 ideal torque actuators at the arms, and 6 contact spheres per foot [3,15,23]) while calibrating the foot-ground contact model. We identified muscle excitations and contact sphere parameters (locations and radii) that minimized a weighted sum of muscle effort (i.e., squared muscle activations) and the difference between measured and simulated variables (joint angles and torques, and ground reaction forces and torques) while satisfying the musculoskeletal dynamics.…”

Section: Methodsmentioning

confidence: 99%

Algorithmic differentiation improves the computational efficiency of OpenSim-based optimal control simulations of movement

Falisse

Serrancolí

et al. 2019

Preprint

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AbstractAlgorithmic differentiation (AD) is an alternative to finite differences (FD) for evaluating function derivatives. The primarily aim of this study was to demonstrate the computational benefits of using AD instead of FD in OpenSim-based optimal control simulations. The secondary aim was to evaluate computational choices including different AD tools, different linear solvers, and the use of first- or second-order derivatives. First, we enabled the use of AD in OpenSim through a custom source code transformation tool and through the operator overloading tool ADOL-C. Second, we developed an interface between OpenSim and CasADi to perform optimal control simulations. Third, we evaluated computational choices through simulations of perturbed balance, two-dimensional predictive simulations of walking, and three-dimensional tracking simulations of walking. We performed all simulations using direct collocation and implicit differential equations. Using AD through our custom tool was between 1.8 ± 0.1 and 17.8 ± 4.9 times faster than using FD, and between 3.6 ± 0.3 and 12.3 ± 1.3 times faster than using AD through ADOL-C. The linear solver efficiency was problem-dependent and no solver was consistently more efficient. Using second-order derivatives was more efficient for balance simulations but less efficient for walking simulations. The walking simulations were physiologically realistic. These results highlight how the use of AD drastically decreases computational time of optimal control simulations as compared to more common FD. Overall, combining AD with direct collocation and implicit differential equations decreases the computational burden of optimal control simulations, which will facilitate their use for biomechanical applications.

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Muscle Contributions to Propulsion and Support During Running

Cited by 111 publications

References 0 publications

Metabolic cost calculations of gait using musculoskeletal energy models, a comparison study

Metabolic cost calculations of gait using musculoskeletal energy models, a comparison study

Adductor magnus: An EMG investigation into proximal and distal portions and direction specific action

Algorithmic differentiation improves the computational efficiency of OpenSim-based optimal control simulations of movement

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