Surface marker cluster translation, rotation, scaling and deformation: Their contribution to soft tissue artefact and impact on knee joint kinematics

Benoît, Daniel; Damsgaard, Michael; Andersen, Michael Skipper

doi:10.1016/j.jbiomech.2015.02.050

Cited by 73 publications

(56 citation statements)

References 29 publications

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“…Barré et al 4 assessed the RM component of the STA, during treadmill walking, using the Procrustes Superimposition (PS) approach 11 applied on the entire cluster of markers and showed that it represents approximately 80-100% of the STA amplitude. A similar conclusion was reached in three other works: de Rosario et al 19 , who defined the NRM and RM components of the STA as the symmetric and skew-symmetric components of the vector field representing the relative 3 marker displacements; Dumas et al 21 , who used the modal approach 20 to split the STA into additive components representing the RM and NRM cluster geometrical transformations in running; and Benoit et al 5 , who modelled the cluster as resizable and deformable and described the STA components for walking, cutting maneuver, and one-legged hops. However, all cited STA quantifications analyze the kinematics of the entire cluster, and do not account for the fact that different sub-clusters within the large cluster of markers may demonstrate different kinematics 22,23 .…”

Section: Introductionsupporting

confidence: 68%

“…Various different representations of these two contributions of the STA have been suggested 2-4, 19, 20, 23 . Most of the studies have shown that the STA's RM component has a similar 23 or larger magnitude than the STA's NRM component 2,4,5,19,21 . Therefore, commonly used techniques that compensate only for cluster NRM 15-17, 25, 32, 37 are insufficient to fully compensate for the STA effects, while the RM component, considered only in few compensation methods 10,19 , has been recently claimed as the only relevant component to be incorporated in pose estimators that can effectively compensate for the STA 21 .…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, it is noted that since the rotation obtained by the PS approach is used in several other methods 5,9,23 for defining different metrics for defining the STA components, these definitions are also affected by the differ rotation tensor obtained by the PS method and the TCPE method.…”

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confidence: 99%

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Bone Pose Estimation in the Presence of Soft Tissue Artifact Using Triangular Cosserat Point Elements

Solav¹,

Rubin²,

Cereatti³

et al. 2017

Preprint

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Accurate estimation of the position and orientation (pose) of a bone from a cluster of skin markers is limited mostly by the relative motion between the bone and the markers, which is known as the Soft Tissue Artifact (STA). This work presents a method, based on continuum mechanics, to describe the kinematics of a cluster affected by STA. The cluster is characterized by Triangular Cosserat Point Elements (TCPEs) defined by all combinations of three markers.The effects of the STA on the TCPEs are quantified using three parameters describing the strain in each TCPE and the relative rotation and translation between TCPEs. The method was evaluated using previously collected ex-vivo kinematic data. Femur pose was estimated from 12 skin markers on the thigh, while its reference pose was measured using bone pins. Analysis revealed that instantaneous subsets of TCPEs exist which estimate bone position and orientation more accurately than the Procrustes Superimposition applied to the cluster of all markers. It has been shown that some of these parameters correlate well with femur pose errors, which suggests that they can be used to select, at each instant, subsets of TCPEs leading an improved estimation of the underlying bone pose.2

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Section: Introductionsupporting

confidence: 68%

Section: Introductionmentioning

confidence: 99%

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Bone Pose Estimation in the Presence of Soft Tissue Artifact Using Triangular Cosserat Point Elements

Solav¹,

Rubin²,

Cereatti³

et al. 2017

Preprint

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“…According to the recent descriptions of the STA (Andersen et al, 2012;Benoit et al, 2015;Dumas et al, 2015;Grimpampi et al, 2014) and to the wobbling mass models reported in the literature (Alonso et al, 2007;Bélaise et al, 2016;Challis and Pain, 2008;Gittoes et al, 2009;Gruber et al, 1998;Günther et al, 2003;McLean et al, 2003;Wilson et al, 2006), it was useful to retrieve the stiffness matrix corresponding only to the modes defining the marker-cluster geometrical rigid transformations and more specifically to the marker-cluster translations. This stiffness matrix was given by:…”

Section: Vvmentioning

confidence: 99%

Stiffness of a wobbling mass models analysed by a smooth orthogonal decomposition of the skin movement relative to the underlying bone

Dumas

Jacquelin

2017

Journal of Biomechanics

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Please cite this article as: R. Dumas, E. Jacquelin, Stiffness of a wobbling mass models analysed by a smooth orthogonal decomposition of the skin movement relative to the underlying bone, Journal of Biomechanics (2017), doi: http://dx.doi.org/10. 1016/j.jbiomech.2017.06.002 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Stiffness of a wobbling mass models analysed by a smooth orthogonal decomposition of the skin movement relative to the underlying bone AbstractThe so-called soft tissue artefacts and wobbling masses have both been widely studied in biomechanics, however most of the time separately, from either a kinematics or a dynamics point of view. As such, the estimation of the stiffness of the springs connecting the wobbling masses to the rigid-body model of the lower limb, based on the in vivo displacements of the skin relative to the underling bone, has not been performed yet. For this estimation, the displacements of the skin markers in the bone-embedded coordinate systems are viewed as a proxy for the wobbling mass movement.The present study applied a structural vibration analysis method called smooth orthogonal decomposition to estimate this stiffness from retrospective simultaneous measurements of skin and intra-cortical pin markers during running, walking, cutting and hopping.For the translations about the three axes of the bone-embedded coordinate systems, the estimated stiffness coefficients (i.e. between 2.3 kN/m and 55.5 kN/m) as well as the corresponding forces representing the connection between bone and skin (i.e. up to 400 N) and corresponding frequencies (i.e. in the band 10-30 Hz) were in agreement with the literature. Consistently with the STA descriptions, the estimated stiffness coefficients were found subject-and task-specific.

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“…Various studies have shown that the rigid component is greater than the non-rigid component (Andersen et al, 2012;Barré et al, 2013;Benoit et al, 2015). Moreover, the rigid component has been demonstrated to be the only one impacting pose estimation accuracy when using RBLS Dumas et al, 2015;Grimpampi et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Bone orientation and position estimation errors using Cosserat point elements and least squares methods: Application to gait

Solav

Camomilla

Cereatti

et al. 2017

Journal of Biomechanics

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The aim of this study was to analyze the accuracy of bone pose estimation based on sub-clusters of three skinmarkers characterized by triangular Cosserat point elements (TCPEs) and to evaluate the capability of four instantaneous physical parameters, which can be measured non-invasively in-vivo, to identify the most accurate TCPEs. Moreover, TCPE pose estimations were compared with the estimations of two least squares minimization methods applied to the cluster of all markers, using rigid body (RBLS) and homogeneous deformation (HDLS) assumptions. Analysis was performed on previously collected in-vivo treadmill gait data composed of simultaneous measurements of the gold-standard bone pose by bi-plane fluoroscopy tracking the subjects' knee prosthesis and a stereophotogrammetric system tracking skin-markers affected by soft tissue artifact. Femur orientation and position errors estimated from skin-marker clusters were computed for 18 subjects using clusters of up to 35 markers. Results based on gold-standard data revealed that instantaneous subsets of TCPEs exist which estimate the femur pose with reasonable accuracy (median root mean square error during stance/swing: 1.4/2.8 deg for orientation, 1.5/4.2 mm for position). A non-invasive and instantaneous criteria to select accurate TCPEs for pose estimation (4.8/7.3 deg, 5.8/12.3 mm), was compared with RBLS (4.3/6.6 deg, 6.9/16.6 mm) and HDLS (4.6/7.6 deg, 6.7/12.5 mm). Accounting for homogeneous deformation, using HDLS or selected TCPEs, yielded more accurate position estimations than RBLS method, which, conversely, yielded more accurate orientation estimations. Further investigation is required to devise effective criteria for cluster selection that could represent a significant improvement in bone pose estimation accuracy.

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Surface marker cluster translation, rotation, scaling and deformation: Their contribution to soft tissue artefact and impact on knee joint kinematics

Cited by 73 publications

References 29 publications

Bone Pose Estimation in the Presence of Soft Tissue Artifact Using Triangular Cosserat Point Elements

Bone Pose Estimation in the Presence of Soft Tissue Artifact Using Triangular Cosserat Point Elements

Stiffness of a wobbling mass models analysed by a smooth orthogonal decomposition of the skin movement relative to the underlying bone

Bone orientation and position estimation errors using Cosserat point elements and least squares methods: Application to gait

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