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Title: An innovative approach to tibiofemoral joint modelling
Author: Lamberto, Giuliano
ISNI:       0000 0004 6422 0088
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2017
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Musculoskeletal models allow non-invasive predictions of non-directly measurable forces exchanged within the human body in motion. Despite this information has plenty of potential applications, actual adoption of current models is impeded by limitations related to the insufficient number of validation studies or the drastic modelling assumptions often made. This thesis aims to address these limitations developing an innovative approach to the mechanical modelling of the tibiofemoral joint. To achieve this, three main sections are presented: Effects of the soft tissue artefact on current musculoskeletal models – this study used a statistical approach to develop a realistic distribution of soft tissue artefact, which was used to assess the sensitivity of the estimates of three publicly-available musculoskeletal models. Results showed joint-dependent variations, decreasing from hip to ankle, providing awareness for the research community on the investigated models and indications to better interpret simulation outcomes. Modelling the mechanical behaviour of the tibiofemoral joint using compliance matrices – this part of the thesis proposed a method to characterise the tibiofemoral joint mechanical behaviour using a discrete set of compliance matrices. Model calibration and validation was performed using data from ex vivo testing. Accurate results were found in close proximity to where the model was calibrated, opening the way to a more biofidelic joint representations. The developed model was included in the calculation pipeline to estimate joint kinematics using penalty-based method. For this inclusion, validation using in vivo data for these estimates was promising, providing remarkable alternatives to traditional methods. A force-based approach to personalised tibiofemoral models – this section attested on an ex vivo dataset that the model based on compliance matrices can be personalised using data from clinical tests. Since the latter are usually performed in vivo, this opens the way to future exciting applications.
Supervisor: Mazza', Claudia Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available