Conjoint Associate Lecturer at Graduate School of Biomedical Engineering, University of New South Wales
+612 9399 1832
Dr Bart Bolsterlee is a biomedical engineer who studies the generation of force and movement by the human musculoskeletal system in health and disease. In 2014 he completed his PhD at Delft University of Technology (The Netherlands) in biomechanical modelling of the human upper limb. During his PhD he developed and applied several computationally advanced methods that improve our understanding of the biomechanics of human movements.
His current work at NeuRA focuses on diffusion tensor imaging (DTI) techniques to measure muscle structure. He and his team led by Prof Rob Herbert have recently developed novel algorithms to obtain quantitative measurements of muscle architecture by combining information from anatomical MRI and DTI scans. He applies these techniques to study mechanisms of muscle contracture (stiffening of muscles) in patients with stroke and cerebral palsy. He also performs studies in basic muscle physiology and biomechanics to elucidate the mechanical role of active and passive structures in muscles.
Dr Bart Bolsterlee’s work has been published in high-quality peer-reviewed journals (Journal of Biomechanics, Clinical Biomechanics, PlosONE).
DR MARTIN HEROUX Research Officer
DR PETER STUBBS Research Officer
: +612 9399 1832
This paper aims to develop an EMG-driven model of the shoulder that can consider possible muscle co-contractions. A musculoskeletal shoulder model (the original model) is modified such that measured EMGs can be used as model-inputs (the EMG-driven model). The model is validated by using the in-vivo measured glenohumeral-joint reaction forces (GH-JRFs). Three patients carrying instrumented hemi-arthroplasty were asked to perform arm abduction and forward-flexion up to maximum possible elevation, during which motion data, EMG, and in-vivo GH-JRF were measured. The measured EMGs were normalized and together with analyzed motions served as model inputs to estimate the GH-JRF. All possible combinations of input EMGs ranging from a single signal to all EMG signals together were tested. The 'best solution' was defined as the combination of EMGs which yielded the closest match between the model and the experiments. Two types of inconsistencies between the original model and the measurements were observed including a general GH-JRF underestimation and a GH-JRF drop above 90° elevation. Both inconsistencies appeared to be related to co-contraction since inclusion of EMGs could significantly (p<.05) improve the predicted GH-JRF (up to 45%). The developed model has shown the potential to successfully take the existent muscle co-contractions of patients into account.
Patient-specific biomechanical models including patient-specific finite-element (FE) models are considered potentially important tools for providing personalized healthcare to patients with musculoskeletal diseases. A multi-step procedure is often needed to generate a patient-specific FE model. As all involved steps are associated with certain levels of uncertainty, it is important to study how the uncertainties of individual components propagate to final simulation results. In this study, we considered a specific case of this problem where the uncertainties of the involved steps were known and the aim was to determine the uncertainty of the predicted strain distribution. The effects of uncertainties of three important components of patient-specific models, including bone density, musculoskeletal loads and the parameters of the material mapping relationship on the predicted strain distributions, were studied. It was found that the number of uncertain components and the level of their uncertainty determine the uncertainty of simulation results. The 'average' uncertainty values were found to be relatively small even for high levels of uncertainty in the components of the model. The 'maximum' uncertainty values were, however, quite high and occurred in the areas of the scapula that are of the greatest clinical relevance. In addition, the uncertainty of the simulation result was found to be dependent on the type of movement analysed, with abduction movements presenting consistently lower uncertainty values than flexion movements.