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musculoskeletal geometry computation  (OpenSim Ltd)

 
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    OpenSim Ltd musculoskeletal geometry computation
    Our differentiable <t>musculoskeletal</t> simulator generates the derivatives of the state variables given the state variables (muscle activations a m , torque actuator activations a T , tendon forces F t , generalized positions q and velocities q ˙ ) and the decision variables (skeleton segment scaling factors p s , muscle volume scaling factors p V m u s c l e , muscle excitations e m , torque actuator excitations e T ). This is achieved by evaluating a set of dynamics equations: activation dynamics, torque actuator dynamics, muscle dynamics, and skeleton dynamics. Evaluating muscle and skeleton dynamics depends on the outputs of musculoskeletal geometry computations (i.e., muscle-tendon lengths l mt and velocities l ˙ m t and muscle moment-arm matrices R ) and on the scaled muscle parameters ( p m , scaled ). Since the scaling of the skeleton and muscle volumes are decision variables, we formulated musculoskeletal geometry computation, muscle parameter scaling and skeleton dynamics as a differentiable function of these decision variables. The dotted boxes indicate the parts of the simulator where we turned non-differentiable computation used in OpenSim and Falisse et al. into differentiable computation. Tendon forces are mapped to joint muscle torques ( τ m ) by the moment-arm matrix ( R ). Torque actuator activations are scaled to torque actuator torques ( τ T ) by a scaling factor of 150 . A contact function ( f contact ) based on the Hunt-Crossley contact model gives the generalized forces resulting from contact ( f c ).
    Musculoskeletal Geometry Computation, supplied by OpenSim Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/musculoskeletal geometry computation/product/OpenSim Ltd
    Average 90 stars, based on 1 article reviews
    musculoskeletal geometry computation - by Bioz Stars, 2026-05
    90/100 stars

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    1) Product Images from "A simulation framework to determine optimal strength training and musculoskeletal geometry for sprinting and distance running"

    Article Title: A simulation framework to determine optimal strength training and musculoskeletal geometry for sprinting and distance running

    Journal: PLOS Computational Biology

    doi: 10.1371/journal.pcbi.1011410

    Our differentiable musculoskeletal simulator generates the derivatives of the state variables given the state variables (muscle activations a m , torque actuator activations a T , tendon forces F t , generalized positions q and velocities q ˙ ) and the decision variables (skeleton segment scaling factors p s , muscle volume scaling factors p V m u s c l e , muscle excitations e m , torque actuator excitations e T ). This is achieved by evaluating a set of dynamics equations: activation dynamics, torque actuator dynamics, muscle dynamics, and skeleton dynamics. Evaluating muscle and skeleton dynamics depends on the outputs of musculoskeletal geometry computations (i.e., muscle-tendon lengths l mt and velocities l ˙ m t and muscle moment-arm matrices R ) and on the scaled muscle parameters ( p m , scaled ). Since the scaling of the skeleton and muscle volumes are decision variables, we formulated musculoskeletal geometry computation, muscle parameter scaling and skeleton dynamics as a differentiable function of these decision variables. The dotted boxes indicate the parts of the simulator where we turned non-differentiable computation used in OpenSim and Falisse et al. into differentiable computation. Tendon forces are mapped to joint muscle torques ( τ m ) by the moment-arm matrix ( R ). Torque actuator activations are scaled to torque actuator torques ( τ T ) by a scaling factor of 150 . A contact function ( f contact ) based on the Hunt-Crossley contact model gives the generalized forces resulting from contact ( f c ).
    Figure Legend Snippet: Our differentiable musculoskeletal simulator generates the derivatives of the state variables given the state variables (muscle activations a m , torque actuator activations a T , tendon forces F t , generalized positions q and velocities q ˙ ) and the decision variables (skeleton segment scaling factors p s , muscle volume scaling factors p V m u s c l e , muscle excitations e m , torque actuator excitations e T ). This is achieved by evaluating a set of dynamics equations: activation dynamics, torque actuator dynamics, muscle dynamics, and skeleton dynamics. Evaluating muscle and skeleton dynamics depends on the outputs of musculoskeletal geometry computations (i.e., muscle-tendon lengths l mt and velocities l ˙ m t and muscle moment-arm matrices R ) and on the scaled muscle parameters ( p m , scaled ). Since the scaling of the skeleton and muscle volumes are decision variables, we formulated musculoskeletal geometry computation, muscle parameter scaling and skeleton dynamics as a differentiable function of these decision variables. The dotted boxes indicate the parts of the simulator where we turned non-differentiable computation used in OpenSim and Falisse et al. into differentiable computation. Tendon forces are mapped to joint muscle torques ( τ m ) by the moment-arm matrix ( R ). Torque actuator activations are scaled to torque actuator torques ( τ T ) by a scaling factor of 150 . A contact function ( f contact ) based on the Hunt-Crossley contact model gives the generalized forces resulting from contact ( f c ).

    Techniques Used: Activation Assay



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    musculoskeletal geometry computation  (OpenSim Ltd)
    90
    OpenSim Ltd musculoskeletal geometry computation
    Our differentiable <t>musculoskeletal</t> simulator generates the derivatives of the state variables given the state variables (muscle activations a m , torque actuator activations a T , tendon forces F t , generalized positions q and velocities q ˙ ) and the decision variables (skeleton segment scaling factors p s , muscle volume scaling factors p V m u s c l e , muscle excitations e m , torque actuator excitations e T ). This is achieved by evaluating a set of dynamics equations: activation dynamics, torque actuator dynamics, muscle dynamics, and skeleton dynamics. Evaluating muscle and skeleton dynamics depends on the outputs of musculoskeletal geometry computations (i.e., muscle-tendon lengths l mt and velocities l ˙ m t and muscle moment-arm matrices R ) and on the scaled muscle parameters ( p m , scaled ). Since the scaling of the skeleton and muscle volumes are decision variables, we formulated musculoskeletal geometry computation, muscle parameter scaling and skeleton dynamics as a differentiable function of these decision variables. The dotted boxes indicate the parts of the simulator where we turned non-differentiable computation used in OpenSim and Falisse et al. into differentiable computation. Tendon forces are mapped to joint muscle torques ( τ m ) by the moment-arm matrix ( R ). Torque actuator activations are scaled to torque actuator torques ( τ T ) by a scaling factor of 150 . A contact function ( f contact ) based on the Hunt-Crossley contact model gives the generalized forces resulting from contact ( f c ).
    Musculoskeletal Geometry Computation, supplied by OpenSim Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/musculoskeletal geometry computation/product/OpenSim Ltd
    Average 90 stars, based on 1 article reviews
    musculoskeletal geometry computation - by Bioz Stars, 2026-05
    90/100 stars
    		  Buy from Supplier

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    Our differentiable musculoskeletal simulator generates the derivatives of the state variables given the state variables (muscle activations a m , torque actuator activations a T , tendon forces F t , generalized positions q and velocities q ˙ ) and the decision variables (skeleton segment scaling factors p s , muscle volume scaling factors p V m u s c l e , muscle excitations e m , torque actuator excitations e T ). This is achieved by evaluating a set of dynamics equations: activation dynamics, torque actuator dynamics, muscle dynamics, and skeleton dynamics. Evaluating muscle and skeleton dynamics depends on the outputs of musculoskeletal geometry computations (i.e., muscle-tendon lengths l mt and velocities l ˙ m t and muscle moment-arm matrices R ) and on the scaled muscle parameters ( p m , scaled ). Since the scaling of the skeleton and muscle volumes are decision variables, we formulated musculoskeletal geometry computation, muscle parameter scaling and skeleton dynamics as a differentiable function of these decision variables. The dotted boxes indicate the parts of the simulator where we turned non-differentiable computation used in OpenSim and Falisse et al. into differentiable computation. Tendon forces are mapped to joint muscle torques ( τ m ) by the moment-arm matrix ( R ). Torque actuator activations are scaled to torque actuator torques ( τ T ) by a scaling factor of 150 . A contact function ( f contact ) based on the Hunt-Crossley contact model gives the generalized forces resulting from contact ( f c ).

    Journal: PLOS Computational Biology

    Article Title: A simulation framework to determine optimal strength training and musculoskeletal geometry for sprinting and distance running

    doi: 10.1371/journal.pcbi.1011410

    Figure Lengend Snippet: Our differentiable musculoskeletal simulator generates the derivatives of the state variables given the state variables (muscle activations a m , torque actuator activations a T , tendon forces F t , generalized positions q and velocities q ˙ ) and the decision variables (skeleton segment scaling factors p s , muscle volume scaling factors p V m u s c l e , muscle excitations e m , torque actuator excitations e T ). This is achieved by evaluating a set of dynamics equations: activation dynamics, torque actuator dynamics, muscle dynamics, and skeleton dynamics. Evaluating muscle and skeleton dynamics depends on the outputs of musculoskeletal geometry computations (i.e., muscle-tendon lengths l mt and velocities l ˙ m t and muscle moment-arm matrices R ) and on the scaled muscle parameters ( p m , scaled ). Since the scaling of the skeleton and muscle volumes are decision variables, we formulated musculoskeletal geometry computation, muscle parameter scaling and skeleton dynamics as a differentiable function of these decision variables. The dotted boxes indicate the parts of the simulator where we turned non-differentiable computation used in OpenSim and Falisse et al. into differentiable computation. Tendon forces are mapped to joint muscle torques ( τ m ) by the moment-arm matrix ( R ). Torque actuator activations are scaled to torque actuator torques ( τ T ) by a scaling factor of 150 . A contact function ( f contact ) based on the Hunt-Crossley contact model gives the generalized forces resulting from contact ( f c ).

    Article Snippet: We implemented the musculoskeletal geometry computation as a differentiable neural network function: l m t , l ̇ m t , R = f l m t , l ̇ m t , R q , q ̇ , p s . Musculoskeletal geometry computation in OpenSim is executed as follows: first, based on the skeleton segment scaling factors the bone geometries, muscle attachment points, muscle via points and muscle wrapping surfaces are adapted, next, using the scaled geometry the muscle-tendon lengths and moment arms are calculated.

    Techniques: Activation Assay