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    Effect of considering stability requirements on antagonistic muscle activities using a musculoskeletal model of the human lumbar spine

    , Article 2013 20th Iranian Conference on Biomedical Engineering, ICBME 2013 ; 2013 , Pages 260-264 Hajihoseinali, M ; Nickpour, H ; Arjmand, N ; Farahmand, F ; Sharif University of Technology
    2013
    Abstract
    The recruitment pattern of trunk muscles is determined using a three-dimensional model of the spine with two joints and six symmetric pairs of muscles in which both equilibrium and stability requirements are satisfied. Model predictions are verified using Anybody Modeling System (AMS) and Abaqus. The model is used to test the hypothesis that antagonistic muscle activities are necessary for the spinal stability. The model with stability constraints predicts muscle activities greater than those predicted without stability consideration. In agreement with experimental data, the stability-based model predicts antagonistic muscle activities. It is shown that spinal stability increases with trunk... 

    Muscles force patterns prediction and joint reactions determination during 3D spine movements by means of optimal control theory

    , Article 2008 International Conference on Control, Automation and Systems, ICCAS 2008, Seoul, 14 October 2008 through 17 October 2008 ; December , 2008 , Pages 548-553 ; 9788995003893 (ISBN) Shahvarpour, A ; Pourtakdoust, H ; Vossoughi, G. R ; Kim, J. Y ; Parnianpour, M ; Sharif University of Technology
    2008
    Abstract
    Modeling the spine behavior plays a key role in understanding mechanisms leading to spinal disorders and injuries. The goal of this article is to develop a new model to obtain 3D spine movement patterns. High degrees of freedom of the system, a great many of muscles involved in the spine movements and its unstable intrinsic behavior make the control problem more difficult. New theories in computational motor control suggest optimal control as a useful tool for modeling Central Nervous System (CNS) to provide appropriate control signals while it computes the neural interactions with biological sensors. Therefore, CNS is modeled as an optimal controller. Our simulations illustrate the movement...