The application of PCA as a movement pattern recognition technique: A proof of principle

Quantitative methods can help us understand how underlying attributes contribute to movement patterns. Applying principal components analysis (PCA) to whole-body motion data may provide an objective data-driven method to identify unique and statistically important movement patterns. Therefore, the primary purpose of this study was to determine if athletes’ movement patterns can be differentiated based on skill level or sport played using PCA. Motion capture data from 542 athletes performing three sport-screening movements (i.e. bird-dog, drop jump, T-balance) were analyzed. A PCA-based pattern recognition technique was used to analyze the data. Prior to analyzing the effects of skill level or sport on movement patterns, methodological considerations related to motion analysis reference coordinate system were assessed. All analyses were addressed as case-studies. For the first case study, referencing motion data to a global (lab-based) coordinate system compared to a local (segment-based) coordinate system affected the ability to interpret important movement features. Furthermore, for the second case study, where the interpretability of PCs was assessed when data were referenced to a stationary versus a moving segment-based coordinate system, PCs were more interpretable when data were referenced to a stationary coordinate system for both the bird-dog and T-balance task. As a result of the findings from case study 1 and 2, only stationary segment-based coordinate systems were used in cases 3 and 4. During the bird-dog task, elite athletes had significantly lower scores compared to recreational athletes for principal component (PC) 1. For the T-balance movement, elite athletes had significantly lower scores compared to recreational athletes for PC 2. In both analyses the lower scores in elite athletes represented a greater range of motion. Finally, case study 4 reported differences in athletes’ movement patterns who competed in different sports, and significant differences in technique were detected during the bird-dog task. Through these case studies, this thesis highlights the feasibility of applying PCA as a movement pattern recognition technique in athletes. Future research can build on this proof-of-principle work to develop robust quantitative methods to help us better understand how underlying attributes (e.g. height, sex, ability, injury history, training type) contribute to performance.

Axis Mapping: The Estimation of Surface Orientations and its Applications in Vehicle Localization and Structural Geology

The map representation of an environment should be selected based on its intended application. For example, a geometrically accurate map describing the Euclidean space of an environment is not necessarily the best choice if only a small subset its features are required. One possible subset is the orientations of the flat surfaces in the environment, represented by a special parameterization of normal vectors called axes. Devoid of positional information, the entries of an axis map form a non-injective relationship with the flat surfaces in the environment, which results in physically distinct flat surfaces being represented by a single axis. This drastically reduces the complexity of the map, but retains important information about the environment that can be used in meaningful applications in both two and three dimensions. This thesis presents axis mapping, which is an algorithm that accurately and automatically estimates an axis map of an environment based on sensor measurements collected by a mobile platform. Furthermore, two major applications of axis maps are developed and implemented. First, the LiDAR compass is a heading estimation algorithm that compares measurements of axes with an axis map of the environment. Pairing the LiDAR compass with simple translation measurements forms the basis for an accurate two-dimensional localization algorithm. It is shown that this algorithm eliminates the growth of heading error in both indoor and outdoor environments, resulting in accurate localization over long distances. Second, in the context of geotechnical engineering, a three-dimensional axis map is called a stereonet, which is used as a tool to examine the strength and stability of a rock face. Axis mapping provides a novel approach to create accurate stereonets safely, rapidly, and inexpensively compared to established methods. The non-injective property of axis maps is leveraged to probabilistically describe the relationships between non-sequential measurements of the rock face. The automatic estimation of stereonets was tested in three separate outdoor environments. It is shown that axis mapping can accurately estimate stereonets while improving safety, requiring significantly less time and effort, and lowering costs compared to traditional and current state-of-the-art approaches.

Computational models for improved diagnosis and prognosis of stroke using robot-based biomarkers

Stroke is a leading cause of death and permanent disability worldwide, affecting millions of individuals. Traditional clinical scores for assessment of stroke-related impairments are inherently subjective and limited by inter-rater and intra-rater reliability, as well as floor and ceiling effects. In contrast, robotic technologies provide objective, highly repeatable tools for quantification of neurological impairments following stroke. KINARM is an exoskeleton robotic device that provides objective, reliable tools for assessment of sensorimotor, proprioceptive and cognitive brain function by means of a battery of behavioral tasks. As such, KINARM is particularly useful for assessment of neurological impairments following stroke. This thesis introduces a computational framework for assessment of neurological impairments using the data provided by KINARM. This is done by achieving two main objectives. First, to investigate how robotic measurements can be used to estimate current and future abilities to perform daily activities for subjects with stroke. We are able to predict clinical scores related to activities of daily living at present and future time points using a set of robotic biomarkers. The findings of this analysis provide a proof of principle that robotic evaluation can be an effective tool for clinical decision support and target-based rehabilitation therapy. The second main objective of this thesis is to address the emerging problem of long assessment time, which can potentially lead to fatigue when assessing subjects with stroke. To address this issue, we examine two time reduction strategies. The first strategy focuses on task selection, whereby KINARM tasks are arranged in a hierarchical structure so that an earlier task in the assessment procedure can be used to decide whether or not subsequent tasks should be performed. The second strategy focuses on time reduction on the longest two individual KINARM tasks. Both reduction strategies are shown to provide significant time savings, ranging from 30% to 90% using task selection and 50% using individual task reductions, thereby establishing a framework for reduction of assessment time on a broader set of KINARM tasks. All in all, findings of this thesis establish an improved platform for diagnosis and prognosis of stroke using robot-based biomarkers.

Targeting the actin cytoskeleton in HER2+ metastatic cancer cells using a macrolide toxin

Breast and ovarian cancers are among the leading causes of cancer related deaths in women worldwide. In a subset of these cancers, dysregulation of the human epidermal growth factor receptor 2 (HER2) leads to overexpression of the receptor on the cell surface. Previous studies have found that these HER2+ cancers show high rates of progression to metastatic disease. Metastasis is driven by cytoskeletal rearrangements that produce filamentous actin (F-actin) based structures that penetrate and degrade extracellular matrix to facilitate tumour invasion. Advancements in targeted therapy have made F-actin an attractive target for the development of new cancer therapies. In this thesis, we tested the actin-depolymerizing macrolide toxin, Mycalolide B (MycB), as a potential warhead for a novel antibody drug conjugate (ADC) to target highly metastatic HER2+ breast and ovarian cancers. We found that MycB treatment of HER2+ breast (SKBR3, MDA-MB-453) and ovarian (SKOV3) cancer cells led to loss of viability (IC50 values ≤ 64 nM). Sub-lethal doses of MycB treatment caused potent suppression of leading edge protrusions, migration and invasion potential of HER2+ cancer cells (IC50 ≤ 32 nM). In contrast, other F-actin based processes such as receptor endocytosis were less sensitive to MycB treatment. MycB treatment skewed the size of endocytic vesicles, which may reflect defects in F-actin based vesicle motility or maturation. Given that HER2+ cancers have been effectively targeted by Trastuzumab and Trastuzumab-based ADCs, we tested the effects of a combination of Trastuzumab and MycB on cell migration and invasion. We found that MycB/ Trastuzumab combination treatments inhibited motility of SKOV3 cells to a greater degree than either treatment alone. Altogether, our results provide proof-of-principle that actin toxins such as MycB can be used as a novel class of warheads for ADCs to target and combat highly metastatic cancers.

Between Principle and Practicality: A Dynamic Realist Examination of Independence in the Canadian Justice System

This work examines independence in the Canadian justice system using an approach adapted from new legal realist scholarship called ‘dynamic realism’. This approach proposes that issues in law must be considered in relation to their recursive and simultaneous development with historic, social and political events. Such events describe ‘law in action’ and more holistically demonstrate principles like independence, rule of law and access to justice. My dynamic realist analysis of independence in the justice system employs a range methodological tools and approaches from the social sciences, including: historical and historiographical study; public administrative; policy and institutional analysis; an empirical component; as well as constitutional, statutory interpretation and jurisprudential analysis. In my view, principles like independence represent aspirational ideals in law which can be better understood by examining how they manifest in legal culture and in the legal system. This examination focuses on the principle and practice of independence for both lawyers and judges in the justice system, but highlights the independence of the Bar. It considers the inter-relation between lawyer independence and the ongoing refinement of judicial independence in Canadian law. It also considers both independence of the Bar and the Judiciary in the context of the administration of justice, and practically illustrates the interaction between these principles through a case study of a specific aspect of the court system. This work also focuses on recent developments in the principle of Bar independence and its relation to an emerging school of professionalism scholarship in Canada. The work concludes by describing the principle of independence as both conditional and dynamic, but rooted in a unitary concept for both lawyers and judges. In short, independence can be defined as impartiality, neutrality and autonomy of legal decision-makers in the justice system to apply, protect and improve the law for what has become its primary normative purpose: facilitating access to justice. While both independence of the Bar and the Judiciary are required to support access to independent courts, some recent developments suggest the practical interactions between independence and access need to be the subject of further research, to better account for both the principles and the practicalities of the Canadian justice system.