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Importance of consistent datasets in musculoskeletal modelling: a study of the hand and wrist

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Title: Importance of consistent datasets in musculoskeletal modelling: a study of the hand and wrist
Authors: Goislard de Monsabert, B
Edwards, T
Shah, DS
Kedgley, AE
Item Type: Journal Article
Abstract: Hand musculoskeletal models provide a valuable insight into the loads withstood by the upper limb; however, their development remains challenging because there are few datasets describing both the musculoskeletal geometry and muscle morphology from the elbow to the finger tips. Clinical imaging, optical motion capture and microscopy were used to create a dataset from a single specimen. Subsequently, a musculoskeletal model of the wrist was developed based on these data to estimate muscle tensions and to demonstrate the potential of the provided parameters. Tendon excursions and moment arms predicted by this model were in agreement with previously reported experimental data. When simulating a flexion-extension motion, muscle forces reached 90 N among extensors and a co-contraction of flexors, amounting to 62.6 N, was estimated by the model. Two alternative musculoskeletal models were also created based on anatomical data available in the literature to illustrate the effect of combining incomplete datasets. Compared to the initial model, the intensities and load sharing of the muscles estimated by the two alternative models differed by up to 180% for a single muscle. This confirms the importance of using a single source of anatomical data when developing such models.
Issue Date: 1-Jan-2018
Date of Acceptance: 19-Sep-2017
URI: http://hdl.handle.net/10044/1/51042
DOI: https://dx.doi.org/10.1007/s10439-017-1936-z
ISSN: 0090-6964
Publisher: Springer Verlag
Start Page: 71
End Page: 85
Journal / Book Title: Annals of Biomedical Engineering
Volume: 46
Issue: 1
Copyright Statement: © The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Sponsor/Funder: Arthritis Research UK
Arthritis Research UK
The Royal Society
Funder's Grant Number: 20556
20680
RG130400
Keywords: clinical imaging
motion capture
digitization
instantaneous helical axes
sarcomere length
moments arms
tendon excursion
muscle force
11 Medical And Health Sciences
09 Engineering
Biomedical Engineering
Publication Status: Published
Online Publication Date: 2017-10-02
Appears in Collections:Faculty of Engineering
Bioengineering



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