Effect of wobbling mass modeling on joint dynamics during human movements with impacts

Journal of Biomechanical Engineering

2018

Self Cite

Multibody kinematics optimization (MKO) aims to reduce soft tissue artefact (STA) and is a key step in musculoskeletal modeling. The objective of this review was to identify the numerical methods, their validation and performance for the estimation of the human joint kinematics using MKO. Seventy-four papers were extracted from a systematized search in five databases and cross-referencing. Model-derived kinematics were obtained using either constrained optimization or Kalman filtering to minimize the difference between measured (i.e., by skin markers, electromagnetic or inertial sensors) and model-derived positions and/or orientations. While hinge, universal, and spherical joints prevail, advanced models (e.g., parallel and four-bar mechanisms, elastic joint) have been introduced, mainly for the knee and shoulder joints. Models and methods were evaluated using: (i) simulated data based, however, on oversimplified STA and joint models; (ii) reconstruction residual errors, ranging from 4 mm to 40 mm; (iii) sensitivity analyses which highlighted the effect (up to 36 deg and 12 mm) of model geometrical parameters, joint models, and computational methods; (iv) comparison with other approaches (i.e., single body kinematics optimization and nonoptimized kinematics); (v) repeatability studies that showed low intra- and inter-observer variability; and (vi) validation against ground-truth bone kinematics (with errors between 1 deg and 22 deg for tibiofemoral rotations and between 3 deg and 10 deg for glenohumeral rotations). Moreover, MKO was applied to various movements (e.g., walking, running, arm elevation). Additional validations, especially for the upper limb, should be undertaken and we recommend a more systematic approach for the evaluation of MKO. In addition, further model development, scaling, and personalization methods are required to better estimate the secondary degrees-of-freedom (DoF).

Section: Fields Of Applicationmentioning

confidence: 99%

Section: Beyond Kinematicsmentioning

confidence: 99%

Multibody Kinematics Optimization for the Estimation of Upper and Lower Limb Human Joint Kinematics: A Systematized Methodological Review

Begon

Andersen

Journal of Biomechanical Engineering

2018

Self Cite

“…These can be representative of the forces between the rigid-body and the wobbling mass to be introduced in the inverse dynamic computation (Alonso et al, 2007;Günther et al, 2003). In the practical case, without the measurements of intra-cortical pin markers, the translation of the centroid of the marker-cluster can be estimated within a multibody kinematics optimisation (Bélaise et al, 2016;Cerveri et al, 2005;Richard et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

“…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

Journal of Biomechanics

Jacquelin

2017

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.

“…The ultimate purpose of STA assessment is to implement methods in order to improve kinematics estimation. These methods can be used either to define mathematical models that can be embedded in optimal bone pose estimator (Alexander and Andriacchi, 2001;Bonci et al, 2014;Camomilla et al, 2015;Camomilla et al, 2013), to implement functional algorithms for locating joint rotation center (De Rosario et al, 2013) or to assess the dynamic effects of the wobbling mass (Bélaise et al, 2016;Thouze et al, 2015). As STA may not be defined a priori because they are subject-, task-and segment-specific, a calibration (i.e.…”

Section: Introductionmentioning

confidence: 99%

Main component of soft tissue artifact of the upper-limbs with respect to different functional, daily life and sports movements

Blache

Journal of Biomechanics

Lundberg

et al. 2017

Soft tissue artifact (STA) is the main source of error in kinematic estimation of human movements based on skin markers. Our objective was to determine the components of marker displacements that best describe STA of the shoulder and arm (i.e. clavicle, scapula and humerus). Four participants performed arm flexion and rotation, a daily-life and a sports movement. Three pins with reflective markers were inserted into the clavicle, scapula and humerus. In addition, up to seven skin markers were stuck on each segment. STA was described with a modal approach: individual marker displacements or marker-cluster (i.e. translations, rotations, homotheties and stretches) relative to the local segment coordinate system defined by markers secured to the pins. The modes were then ranked according to the percentage of total STA energy that they explained. Both individual skin marker displacements and marker-cluster geometrical transformations were task-, location-, segment- and subject-specific. However, 85% of the total STA energy was systematically explained by the rigid transformations (i.e. translations and rotations of the marker-cluster). In conclusion, large joint dislocations and limited efficiency of least squares bone pose estimators are expected for the computation of upper limb joint kinematics from skin markers. Future developments shall consider the rigid transformations of marker-clusters in the implementation of an STA model to reduce its effects on kinematics estimation.