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Title: Viscoelastic properties of passive skeletal muscle in compression: cyclic behaviour
Sponsor: Higher Education Authority
Author's Homepage:
Keywords: Bioengineering
Passive skeletal muscle
viscoelastic properties
Issue Date: 2009
Publisher: Elsevier
Citation: Van Loocke M, Lyons CG, Simms CK, Viscoelastic properties of passive skeletal muscle in compression: cyclic behaviour, Journal of Biomechanics, 42, 8, 2009, 1038-1048
Series/Report no.: Journal of Biomechanics;
Abstract: The compressive properties of skeletal muscle are important in impact biomechanics, rehabilitation engineering and surgical simulation. However, the mechanical behaviour of muscle tissue in compression remains poorly characterised. In this paper, the time-dependent properties of passive skeletal muscle were investigated using a combined experimental and theoretical approach. Uniaxial ramp and hold compression tests were performed in vitro on fresh porcine skeletal muscle at various rates and orientations of the tissue fibres. Results show that above a very small compression rate, the viscoelastic component plays a significant role in muscle mechanical properties; it represents approximately 50% of the total stress reached at a compression rate of 0.5%s-1. A stiffening effect with compression rate is observed especially in directions closer to the muscle fibres. Skeletal muscle viscoelastic behaviour is thus dependent on compression rate and fibre orientation. A model is proposed to represent the observed experimental behaviour which is based on the quasi-linear viscoelasticity framework. A previously developed Strain dependent Young’s Moduli formulation was extended with Prony series to account for the tissue viscoelastic properties. Parameters of the model were obtained by fitting to stress-relaxation data obtained in the muscle fibre, cross-fibre and 45° directions. The model then successfully predicted stress-relaxation behaviour at 60° from the fibre direction (errors < 25%). Simultaneous fitting to data obtained at compression rates of 0.5, 1 and 10%s-1 was performed and the model provided a good fit to the data as well as good predictions of muscle behaviour at rates of 0.05 and 5%s-1 (errors < 25%).
Description: PUBLISHED
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Appears in Collections:Mechanical & Manufacturing Eng (Scholarly Publications)

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