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

23 Citations (Scopus)


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
Issue number2
Publication statusPublished - 2016
Externally publishedYes


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