The Obstacle-Set Method for Representing Muscle Paths in Musculoskeletal Models

Garner, Brian; Pandy, Marcus G.

doi:10.1080/10255840008915251

Cited by 161 publications

(122 citation statements)

References 39 publications

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“…1(A)). "Via points" and "wrapping surfaces" are commonly defined to geometrically constrain the path from penetrating underlying bones and muscles 15,22,45 ; however, it is frequently unclear how to specify these constraints, and the muscle moment arms can be highly sensitive to how the constraints are defined. It also is challenging to use a series of line segments to represent muscles with broad attachments, like the gluteus maximus ( Fig.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Representation of Complex Muscle Architectures and Geometries

2005

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Abstract-Almost all computer models of the musculoskeletal system represent muscle geometry using a series of line segments. This simplification (i) limits the ability of models to accurately represent the paths of muscles with complex geometry and (ii) assumes that moment arms are equivalent for all fibers within a muscle (or muscle compartment). The goal of this work was to develop and evaluate a new method for creating three-dimensional (3D) finite-element models that represent complex muscle geometry and the variation in moment arms across fibers within a muscle. We created 3D models of the psoas, iliacus, gluteus maximus, and gluteus medius muscles from magnetic resonance (MR) images. Peak fiber moment arms varied substantially among fibers within each muscle (e.g., for the psoas the peak fiber hip flexion moment arms varied from 2 to 3 cm, and for the gluteus maximus the peak fiber hip extension moment arms varied from 1 to 7 cm). Moment arms from the literature were generally within the range of fiber moment arms predicted by the 3D models. The models accurately predicted changes in muscle surface geometry over a 55• range of hip flexion, as compared to changes in shape predicted from MR images (average errors between the model and measured surfaces were between 1.7 and 5.2 mm). This new framework for representing muscle will enhance the accuracy of computer models of the musculoskeletal system.

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Section: Introductionmentioning

confidence: 99%

Three-Dimensional Representation of Complex Muscle Architectures and Geometries

2005

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“…Therefore, a three-dimensional mathematical musculoskeletal shoulder model [12,21,22] was used to represent the geometry structure of the skeleton and muscles of the shoulder and calculate the muscle activate state under a specific external load. The geometric parameters of the model were developed on the basis of the CT images of bones and muscles collected from the Visible Human Project (VHP) database [21].…”

Section: Musculoskeletal Model Of Shoulder Complexmentioning

confidence: 99%

“…Therefore, total 31 muscle bundles were used to represent the muscles that contributed to the function of the shoulder (see in Table 1). The muscle path was determined using the obstacle-set method proposed by Garner and Pandy [22], so that the muscle lines of action could be determined.…”

Section: Musculoskeletal Model Of Shoulder Complexmentioning

confidence: 99%

“…However, these musculoskeletal models based on the anatomical structure have not been applied in the rehabilitation robots adequately [9,20]. Therefore, in this paper, a three-dimensional mathematical musculoskeletal model [12,21,22] of the shoulder complex was used to represent the relationship of the muscles and external loads in determining the training strategy for the muscle-specific rehabilitation.…”

Section: Introductionmentioning

confidence: 99%

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A Muscle-Specific Rehabilitation Training Method of the Human Shoulder Based on the Optimal Load Orientation Concept

Nie¹,

Song²,

Kang³

et al. 2016

Proceedings of the 2016 International Conference on Biomedical and Biological Engineering

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Abstract. In order to implement the high-efficiency resistance training for a specific muscle of human shoulders using the rehabilitation robots, a muscle-specific rehabilitation training method based on the optimal load orientation concept (OLOC) was proposed. A 3D mathematical musculoskeletal model of the shoulder complex was used to predict the muscle forces. In this model, 31 muscle bundles were used to represent all the muscles contributing to the shoulder function, and the Hill-type model was used to characterize the mechanical property of the muscles. The calculation results show that, for a specific muscle, there is always an optimal load orientation (OLO) of the external load which lead the activation of muscle to its maximum. Moreover, the distribution of the OLO is significantly consistent as the movement under different magnitudes of load. Thus the optimal load orientation cluster for a specific muscle, which can be used to specify a muscle-specific rehabilitation strategy, was determined. Simultaneously, the analysis suggests that the muscle-specific rehabilitation training method based on the OLOC could improve the training efficiency of specific muscles significantly.

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“…The most common and traditional models (e.g. (Garner and Pandy, 2000), (Delp and Loan, 2000)) assume that the muscle mechanical action occurs along a poly-line, namely the action line, joining origin and insertion points of the muscle, i.e., sites at which the muscle is attached to the bone by a tendon. Essentially, an action line is a representation of fibres in muscle-tendon unit.…”

Section: Introductionmentioning

confidence: 99%

Fast Deformation for Modelling of Musculoskeletal System

Kohout

Kellnhofer

Martelli

2012

Proceedings of the International Conference on Computer Graphics Theory and Applications

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Abstract:This paper proposes a gradient domain deformation for wrapping surface models of muscles around bones as they move during a simulation of physiological activities. Each muscle is associated with one or more poly-lines that represent the muscle skeleton to which the surface model of the muscle is bound so that transformation of the skeleton (caused by the movement of bones) produces transformation of the vertices of the mesh subject to Laplacian linear constraints to preserve the local shape of the mesh and non-linear volume constraints to preserve the volume of the mesh. All these constraints form a system of equations that is solved using the iterative Gauss-Newton method with Lagrange multipliers. Our C++ implementation can wrap a muscle of medium size in about a couple of ms up to 400 ms on commodity hardware depending on the type of parallelization, whilst it can keep the change in volume below 0.04%. A preliminary biomechanical assessment of the proposed technique suggests that it can produce realistic results and thanks to its rapid processing speed, it might be an attractive alternative to the methods that are used in clinical practise at present.

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The Obstacle-Set Method for Representing Muscle Paths in Musculoskeletal Models

Cited by 161 publications

References 39 publications

Three-Dimensional Representation of Complex Muscle Architectures and Geometries

Three-Dimensional Representation of Complex Muscle Architectures and Geometries

A Muscle-Specific Rehabilitation Training Method of the Human Shoulder Based on the Optimal Load Orientation Concept

Fast Deformation for Modelling of Musculoskeletal System

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