Literature DB >> 27986326

Kinematic models of the upper limb joints for multibody kinematics optimisation: An overview.

Sonia Duprey1, Alexandre Naaim2, Florent Moissenet3, Mickaël Begon4, Laurence Chèze5.   

Abstract

Soft tissue artefact (STA), i.e. the motion of the skin, fat and muscles gliding on the underlying bone, may lead to a marker position error reaching up to 8.7cm for the particular case of the scapula. Multibody kinematics optimisation (MKO) is one of the most efficient approaches used to reduce STA. It consists in minimising the distance between the positions of experimental markers on a subject skin and the simulated positions of the same markers embedded on a kinematic model. However, the efficiency of MKO directly relies on the chosen kinematic model. This paper proposes an overview of the different upper limb models available in the literature and a discussion about their applicability to MKO. The advantages of each joint model with respect to its biofidelity to functional anatomy are detailed both for the shoulder and the forearm areas. Models capabilities of personalisation and of adaptation to pathological cases are also discussed. Concerning model efficiency in terms of STA reduction in MKO algorithms, a lack of quantitative assessment in the literature is noted. In priority, future studies should concern the evaluation and quantification of STA reduction depending on upper limb joint constraints.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Keywords:  Forearm; Kinematic model; Multibody kinematics optimisation; Shoulder; Upper limb

Mesh:

Year:  2016        PMID: 27986326     DOI: 10.1016/j.jbiomech.2016.12.005

Source DB:  PubMed          Journal:  J Biomech        ISSN: 0021-9290            Impact factor:   2.712


  8 in total

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Journal:  Med Biol Eng Comput       Date:  2018-04-03       Impact factor: 2.602

2.  Uncertainty analysis and sensitivity of scapulothoracic joint angles to kinematic model parameters.

Authors:  Y Blache; I Rogowski; M Degot; R Trama; R Dumas
Journal:  Med Biol Eng Comput       Date:  2022-05-13       Impact factor: 2.602

3.  Effects of realistic sheep elbow kinematics in inverse dynamic simulation.

Authors:  Baptiste Poncery; Santiago Arroyave-Tobón; Elia Picault; Jean-Marc Linares
Journal:  PLoS One       Date:  2019-03-05       Impact factor: 3.240

4.  Lower limb sagittal gait kinematics can be predicted based on walking speed, gender, age and BMI.

Authors:  Florent Moissenet; Fabien Leboeuf; Stéphane Armand
Journal:  Sci Rep       Date:  2019-07-02       Impact factor: 4.379

5.  Human Movement Representation on Multivariate Time Series for Recognition of Professional Gestures and Forecasting Their Trajectories.

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Journal:  Front Robot AI       Date:  2020-08-13

6.  Three-Dimensional Upper Body Kinematics and Inter-articular Kinematic Sequence During a Canoe Polo Throw.

Authors:  Najoua Assila; Cyril Delavallade; Yoann Blache; Christian Berger-Vachon; Philippe Collotte; Sonia Duprey
Journal:  Front Sports Act Living       Date:  2021-12-15

7.  A Matlab toolbox for scaled-generic modeling of shoulder and elbow.

Authors:  Ehsan Sarshari; Yasmine Boulanaache; Alexandre Terrier; Alain Farron; Philippe Mullhaupt; Dominique Pioletti
Journal:  Sci Rep       Date:  2021-10-21       Impact factor: 4.379

8.  A survey of human shoulder functional kinematic representations.

Authors:  Rakesh Krishnan; Niclas Björsell; Elena M Gutierrez-Farewik; Christian Smith
Journal:  Med Biol Eng Comput       Date:  2018-10-26       Impact factor: 2.602

  8 in total

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