Muscular synergism—II. A minimum-fatigue criterion for load sharing between synergistic muscles

Dul, Jan; Johnson, Glen E.; Shiavi, Richard; Townsend, Mardie

doi:10.1016/0021-9290(84)90121-0

Cited by 383 publications

(61 citation statements)

References 14 publications

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“…t-tests were used to test whether the slope was different from predicted values of 1 and 1.12 for optimizations models where force is shared equally or in proportion to physiological cross-sectional area (reviewed in Crowninshield and Brand, 1981;Herzog and Leonard, 1991;Herzog, 1996). For our system a ratio of 1.12 may also support the minimum fatigue optimization model (Dul et al, 1984), since there is no difference in fiber type distribution between the LG and MG in other terrestrial birds (Patak and Baldwin, 1993).…”

Section: Statisticsmentioning

confidence: 56%

“…Pedotti and coworkers (Pedotti et al, 1978) proposed that the distribution of force between synergists at a joint was a function of each muscle's capacity for force production, which was in turn a function of each muscle's contractile properties and instantaneous velocity and length. Dul and coworkers (Dul et al, 1984) proposed that a muscle's susceptibility to fatigue was an important factor in determining force sharing among synergists, so that muscles with a higher percentage of oxidative fibers will bear a greater fraction of the total force required from synergists. A rigorous test of these models requires information about contractile properties, length, velocity during contraction and fiber type of the muscles.…”

Section: Force Sharing In the Medial And Lateral Gastrocnemiusmentioning

confidence: 99%

“…One of the simplest models predicts that force is distributed among muscles according to their capacity to develop force, as measured by their physiological cross-sectional area (Crowninshield, 1978). More complex models have been developed to incorporate the dynamic behavior of muscles during locomotion and the influence of muscle properties on the capacity of each synergist to develop force (Pedotti et al, 1978), or to include the effect of muscle fiber type and fatique susceptibility of different muscle heads on force distribution (Dul et al, 1984). Empirical measurements of force production in muscle synergists (Akima et al, 2002;Biewener and Baudinette, 1995;Biewener and Corning, 2001;Fagg et al, 2002;Herzog and Leonard, 1991;Kaya et al, 2003;Stokes and Gardner-Morse, 2001) do not consistently support any of these models.…”

Section: Introductionmentioning

confidence: 99%

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Task-dependent force sharing between muscle synergists during locomotion in turkeys

Nelson

Roberts

2008

Journal of Experimental Biology

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SUMMARYAt most joints, there is a redundancy of muscle function. For any given movement, there are a wide range of possible solutions to the problem of how force is shared among muscle synergists. A better understanding of how force is shared among muscle synergists can provide insight into the mechanics and control of movement. We examined force sharing in the gastrocnemius of wild turkeys (Meleagris gallopavo), using strain gauges bonded to bony tendons. Force was measured separately in the lateral (LG) and medial (MG) heads of the gastrocnemius, to evaluate force sharing. We also used inverse dynamics to determine the total force required during swing phase. To determine whether the pattern of force sharing varied depending on the mechanical task, we used running speed (1 to 3.5·m·s -1 ) and limb loading (30 and 60·g added tarsometatarsal mass) to vary the force required at the intertarsal joint. We found that the distribution of force between these two heads varied depending on the phase of the stride cycle. During stance, force was shared in near equal amounts between the two heads and this distribution was unaffected by changes in running speed or limb load. During swing phase, however, there was no force sharing. Force was produced only in the lateral head, and this force was not significantly different from the total force required, as calculated from inverse dynamics. Thus, the LG produced all of the force required for limb extension during swing. This change in the pattern of force sharing between stance and swing supports the theory that force sharing between muscle synergists is task-dependent.

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Section: Statisticsmentioning

confidence: 56%

Section: Force Sharing In the Medial And Lateral Gastrocnemiusmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Task-dependent force sharing between muscle synergists during locomotion in turkeys

Nelson

Roberts

2008

Journal of Experimental Biology

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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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“…2. To incorporate muscle models in models of the body(-segments) to simulate human (e.g., Hatze, 1976;Hatze, 1977;Pedotti et al, 1978;Dul et al, 1984;Bobbert et al, 1986a;Bobbert et al, 1986b;Zajac, 1989;Thunnisse, 1993;Franken, 1993) or animal movement (van Leeuwen, 1992) and study its control. The distinguished categories 1 and 2 are similar to Winters' muscle physiologists and system bioscientists' approaches (Winters, 1995).…”

Section: Modelling Goalsmentioning

confidence: 99%

Parameter interdependence and success of skeletal muscle modelling

Huijing

1995

Human Movement Science

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In muscle and movement modelling it is almost invariably assumed that force actually exerted is determined by several independent factors. This review considers the fact that length force characteristics are not a relatively fixed property of muscle but should be considered the product of a substantial number of interacting factors. Level of activation and recruitment are influential factors in relation to aspects of muscle architecture. For the level of activation effects of its short term history (potentiation, fatigue in sustained contractions) have to be taken into account and are reviewed on the basis of recent experimental results as well as available literature. History is also an important determinant for the effect of length changes. This concept is introduced on the basis of recent experimental evidence as well as available literature. Regarding effects of muscle architecture, the concepts of primary and secondary distribution of fibre mean sarcomere length are introduced as well as effects of muscle geometry for mono-and bi-articular muscles on those distributions. Implications for motor control are discussed and the need for intramuscular coordination indicated.

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Muscular synergism—II. A minimum-fatigue criterion for load sharing between synergistic muscles

Cited by 383 publications

References 14 publications

Task-dependent force sharing between muscle synergists during locomotion in turkeys

Task-dependent force sharing between muscle synergists during locomotion in turkeys

Neural Networks for Muscle Forces Prediction in Cycling

Parameter interdependence and success of skeletal muscle modelling

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