A linear soft tissue artefact model for human movement analysis: Proof of concept using in vivo data

Andersen, Michael Skipper; Damsgaard, Michael; Rasmussen, John; Ramsey, Dan K.; Benoît, Daniel

doi:10.1016/j.gaitpost.2011.11.032

Cited by 52 publications

(39 citation statements)

References 27 publications

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“…In terms of the latter, attempts have been made to investigate the sensitivity of joint parameter optimisation to random noise in the marker data (Reinbolt et al 2005). However, Andersen et al (2012) recently demonstrated that marker error caused by STA is neither random nor independent and 95% of the cumulative marker error caused by STA during gait could be modelled with a linear model containing only four parameters. Therefore, it is not completely evident that the lower error exhibited by the Linearly scaled model and the Kinematically scaled model (Figure 3) was a result of the model's ability to capture the underlying subject-specific kinematics.…”

Section: Marker Errorsmentioning

confidence: 99%

Scaling of musculoskeletal models from static and dynamic trials

Lund

Andersen

Zee

et al. 2015

International Biomechanics

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121

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Subject-specific scaling of cadaver-based musculoskeletal models is important for accurate musculoskeletal analysis within multiple areas such as ergonomics, orthopaedics and occupational health. We present two procedures to scale 'generic' musculoskeletal models to match segment lengths and joint parameters to a specific subject and compare the results to a simpler approach based on linear, segment-wise scaling. By incorporating data from functional and standing reference trials, the new scaling approaches reduce the model sensitivity to assumed model marker positions. For validation, we applied all three scaling methods to an inverse dynamics-based musculoskeletal model and compared predicted knee joint contact forces to those measured with an instrumented prosthesis during gait. Additionally, a Monte Carlo study was used to investigate the sensitivity of the knee joint contact force to random adjustments of the assumed model marker positions (+/− one marker diameter). The model based on linear scaling showed the highest variation in the knee joint contact force of 1.44 body weight (BW) around contra-lateral heel strike, and a variation in root mean square deviation (RMSD) of 0.36 BW. The proposed methods reduced the variation to 1.0 BW (RMSD 0.26 BW) for the anatomical landmark based method and 0.47 BW (RMSD 0.06 BW) for the functional based method. Variation in model predictions due to uncertainty in marker positions is a trait of all marker-based musculoskeletal modelling approaches. The presented methods solve part of this problem and rely less on manual identification of anatomical landmarks in the model. The work represents a step towards a more consistent methodology in musculoskeletal modelling.

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Section: Marker Errorsmentioning

confidence: 99%

Scaling of musculoskeletal models from static and dynamic trials

Lund

Andersen

Zee

et al. 2015

International Biomechanics

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121

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“…1) (Table 1), it can be recognized (i.e. (Lafortune et al, 1995;Pain and Challis, 2006;Schmitt and Günther, 2011;Wakeling et al, 2003) Using the same walking, cutting, and hopping data from Benoit et al (2006) as in the present study, Andersen et al (2012) applied a principal component analysis and demonstrated that the displacements of the skin markers relative to the underling bone can be represented by a linear combination of a low number of components. Similarly, using the same running data from Reinschmidt et al (1997), Dumas et al (2014b) applied a proper orthogonal decomposition and demonstrated that the displacements of the skin markers relative to the underling bone can be represented by a linear combination of a low number of modes.…”

Section: Discussionmentioning

confidence: 68%

“…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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A linear soft tissue artefact model for human movement analysis: Proof of concept using in vivo data

Cited by 52 publications

References 27 publications

Scaling of musculoskeletal models from static and dynamic trials

Scaling of musculoskeletal models from static and dynamic trials

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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