Multiscale musculoskeletal modelling, data-model fusion and electromyography-informed modelling

Justin Fernandez, Ju Zhang, T Heidlauf, M Sartori, Thor Besier, O Röhrle, D Lloyd

Research output: Contribution to journalArticleAcademicpeer-review

22 Citations (Scopus)

Abstract

This paper proposes methods and technologies that advance the state of the art for modelling the musculoskeletal system across the spatial and temporal scales; and storing these using efficient ontologies and tools. We present population-based modelling as an efficient method to rapidly generate individual morphology from only a few measurements and to learn from the ever-increasing supply of imaging data available. We present multiscale methods for continuum muscle and bone models; and efficient mechanostatistical methods, both continuum and particle-based, to bridge the scales. Finally, we examine both the importance that muscles play in bone remodelling stimuli and the latest muscle force prediction methods that use electromyography-assisted modelling techniques to compute musculoskeletal forces that best reflect the underlying neuromuscular activity. Our proposal is that, in order to have a clinically relevant virtual physiological human, (i) bone and muscle mechanics must be considered together; (ii) models should be trained on population data to permit rapid generation and use underlying principal modes that describe both muscle patterns and morphology; and (iii) these tools need to be available in an open-source repository so that the scientific community may use, personalize and contribute to the database of models.
Original languageEnglish
Article number20150084
Number of pages11
JournalInterface focus
Volume6
Issue number2
DOIs
Publication statusPublished - 2016
Externally publishedYes

Fingerprint

Dive into the research topics of 'Multiscale musculoskeletal modelling, data-model fusion and electromyography-informed modelling'. Together they form a unique fingerprint.

Cite this