A physiologically based criterion of muscle force prediction in locomotion

Crowninshield, Roy D.; Brand, Richard A.

doi:10.1016/0021-9290(81)90035-x

Cited by 1,256 publications

(720 citation statements)

References 10 publications

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“…Optimization models attempt to overcome this problem by minimising or maximising a physiologic criterion (cost function), and yield a unique set of muscle forces from a previously indeterminable set. The cost function proposed by Crowninshield and Brand [9] to predict muscle activation with the aim of minimising muscle fatigue was used in the current model, and has been validated for this purpose previously [10,11]. Skeletal and muscular trunk anatomy input data were derived from a previous study describing 180 muscles [48].…”

Section: Load Estimationsmentioning

confidence: 99%

The effect of osteoporotic vertebral fracture on predicted spinal loads in vivo

et al. 2006

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The aetiology of osteoporotic vertebral fractures is multi-factorial, and cannot be explained solely by low bone mass. After sustaining an initial vertebral fracture, the risk of subsequent fracture increases greatly. Examination of physiologic loads imposed on vertebral bodies may help to explain a mechanism underlying this fracture cascade. This study tested the hypothesis that model-derived segmental vertebral loading is greater in individuals who have sustained an osteoporotic vertebral fracture compared to those with osteoporosis and no history of fracture. Flexion moments, and compression and shear loads were calculated from T2 to L5 in 12 participants with fractures (66.4 ± 6.4 years, 162.2 ± 5.1 cm, 69.1 ± 11.2 kg) and 19 without fractures (62.9 ± 7.9 years, 158.3 ± 4.4 cm, 59.3 ± 8.9 kg) while standing. Static analysis was used to solve gravitational loads while muscle-derived forces were calculated using a detailed trunk muscle model driven by optimization with a cost function set to minimise muscle fatigue. Least squares regression was used to derive polynomial functions to describe normalised load profiles. Regression co-efficients were compared between groups to examine differences in loading profiles. Loading at the fractured level, and at one level above and below, were also compared between groups. The fracture group had significantly greater normalised compression (p = 0.0008) and shear force (p < 0.0001) profiles and a trend for a greater flexion moment profile. At the level of fracture, a significantly greater flexion moment (p = 0.001) and shear force (p < 0.001) was observed in the fracture group. A greater flexion moment (p = 0.003) and compression force (p = 0.007) one level below the fracture, and a greater flexion moment (p = 0.002) and shear force (p = 0.002) one level above the fracture was observed in the fracture group. The differences observed in multi-level spinal loading between the groups may explain a mechanism for increased risk of subsequent vertebral fractures. Interventions aimed at restoring vertebral morphology or reduce thoracic curvature may assist in normalising spine load profiles.

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Section: Load Estimationsmentioning

confidence: 99%

The effect of osteoporotic vertebral fracture on predicted spinal loads in vivo

et al. 2006

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“…While our prior analysis demonstrated that the measured EMG and force components could be correlated via the assumed muscle synergy to endpoint force transformation, we could not demonstrate that this relationship was biomechanically causal. In this study, synergy force vectors identified in the control posture of each animal from the previous work (Torres-Oviedo et al 2006) were used as source data, and simulated muscle synergies corresponding to each hypothesized synergy force vector were determined with numerical optimization (e.g., Crowninshield and Brand 1981;Harris and Wolpert 1998;Kurtzer et al 2006;Valero-Cuevas et al 1998). We then applied these muscle synergies to the model in other postures to test whether the resulting force vectors were oriented consistently with respect to the limb axis.…”

Section: Introductionmentioning

confidence: 99%

Functional muscle synergies constrain force production during postural tasks

McKay

Ting

2008

Journal of Biomechanics

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We recently demonstrated that a set of five functional muscle synergies were sufficient to characterize both hindlimb muscle activity and active forces during automatic postural responses in cats standing at multiple postural configurations. This characterization depended critically upon the assumption that the endpoint force vector (synergy force vector) produced by the activation of each muscle synergy rotated with the limb axis as the hindlimb posture varied in the sagittal plane. Here, we used a detailed, 3D static model of the hindlimb to confirm that this assumption is biomechanically plausible: as we varied the model posture, simulated synergy force vectors rotated monotonically with the limb axis in the parasagittal plane (r 2 = 0.94 ± 0.08). We then tested whether a neural strategy of using these five functional muscle synergies provides the same force-generating capability as controlling each of the 31 muscles individually. We compared feasible force sets (FFS) from the model with and without a muscle synergy organization. FFS volumes were significantly reduced with the muscle synergy organization (F = 1556.01, p ≪ 0.01), and as posture varied, the synergy-limited FFSs changed in shape, consistent with changes in experimentally-measured active forces. In contrast, nominal FFS shapes were invariant with posture, reinforcing prior findings that postural forces cannot be predicted by hindlimb biomechanics alone. We propose that an internal model for postural force generation may coordinate functional muscle synergies that are invariant in intrinsic limb coordinates, and this reduced-dimension control scheme reduces the set of forces available for postural control.

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“…The cubic exponent used in the equation (3), guarantees the best tradeoff between the muscular contractile force and the maximum duration of the contraction. This cost function is widely used in literature [36,37] as it relies on the co-activation of all the muscles involved in the gesture.…”

Section: Segment Body Part Symbolmentioning

confidence: 99%

Neural Networks for Muscle Forces Prediction in Cycling

et al. 2014

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This paper documents the research towards the development of a system based on Artificial Neural Networks to predict muscle force patterns of an athlete during cycling. Two independent inverse problems must be solved for the force estimation: evaluation of the kinematic model and evaluation of the forces distribution along the limb. By solving repeatedly the two inverse problems for different subjects and conditions, a training pattern for an Artificial Neural Network was created. Then, the trained network was validated against an independent validation set, and compared to evaluate agreement between the two alternative approaches using Bland-Altman method. The obtained neural network for the different test patterns yields a normalized error well below 1% and the Bland-Altman plot shows a considerable correlation between the two methods. The new approach proposed herein allows a direct and fast computation for the inverse dynamics of a cyclist, opening the possibility of integrating such algorithm in a real time environment such as an embedded application.

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A physiologically based criterion of muscle force prediction in locomotion

Cited by 1,256 publications

References 10 publications

The effect of osteoporotic vertebral fracture on predicted spinal loads in vivo

The effect of osteoporotic vertebral fracture on predicted spinal loads in vivo

Functional muscle synergies constrain force production during postural tasks

Neural Networks for Muscle Forces Prediction in Cycling

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