Masters Degrees (Mechanical and Mechatronic Engineering)

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Browsing Masters Degrees (Mechanical and Mechatronic Engineering) by Author "Atkinson, Julian David"

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    Computational modelling techniques to determine patellofemoral joint loads.
    (Stellenbosch : Stellenbosch University, 2018-03) Atkinson, Julian David; Muller, Jacobus Hendrik; Stellenbosch University. Faculty of Engineering. Dept. of Mechanical and Mechatronic Engineering.
    ENGLISH ABSTRACT: Neuromechanical computational tools provide insight into the loads around human joints that cannot be measured in vivo. An automated computational pipeline, designed to run with minimal user intervention that outputs joint kinematics, joint moments, muscle parameters and muscle forces is outlined in this project. The tool uses OpenSim (Simtk-confluence.stanford.edu, 2017) to determine the inputs into CEINMS (CEINMS Simtk.org, 2017), which uses this information in conjunction with the electromyographic (EMG) data to inform a neuromusculoskeletal model that outputs muscle forces and adjusted muscle parameters. These computational tools are open source and freely available. Thirty patellofemoral pain (PFP) subjects were tested during the experimental phase of the project with the EMG, ground reaction force (GRF) and kinematic data of five of the subjects, before and after eight weeks of physiotherapeutic intervention, acting as an input to the tool. The results of the tool can then be used to quantify changes in patientsā€™ underlying biomechanics to provide insight into proposed risk factors of PFP. Before clinical questions can be answered using the results obtained from data, it is crucial to determine whether the outputs of the tool are clinically relevant, and to what extent experimental and modelling errors influence the results. The processing techniques used to filter and smooth the GRF, marker and EMG data is investigated. It is shown that filtering the marker and force plate data at different frequencies introduces an artefact at the point of impact for the knee joint moment. Changing EMG filtering frequency is shown to affect the magnitude of muscles forces produced by CEINMS. A sensitivity of the CEINMS optimiser to biarticulate muscles is identified. However, the exact causal relationship is not known and requires further research. The muscle force results of CEINMS are also affected by changing the EMG normalisation technique. Using maximum voluntary contractions (MVC) to normalise the EMGs is shown to produce muscle forces of a different magnitude to using walking trial maximums (WTM). Finally, the results across sessions showed high repeatability for the kinematics, dynamics and muscle forces with a coefficient of determination (COD), š‘…2 of 0.93, 0.93 and 0.83 respectively. All the subjects showed significant change in their muscle forces across sessions (š‘ƒ<0.05) for the majority of their muscles, with only subject AKP 29 showing significant change in less than 14 of the 16 tested muscles. The muscle force results produced shapes that are comparable to literature. While it is concluded that realistic muscle forces are produced, the full extent to which the modelling and experimental errors account for the changes seen between subjects across sessions needs to be further researched. This project presents a semi-automated computational tool that enables joint moment and muscle force estimation from motion laboratory gait data. Recommendations on data capturing, storage and processing are outlined, which are applicable for related studies requiring biomechanical analysis of human joints.