This thesis considers the tasks involved in exploratory analysis of temporal graph data, and the visual techniques which are able to support these tasks. There has been an enormous increase in the amount and availability of graph (network) data, and in particular, graph data that is changing over time. Understanding the mechanisms involved in temporal change in a graph is of interest to a wide range of disciplines. While the application domain may differ, many of the underlying questions regarding the properties of the graph and mechanism of change are the same. The research area of temporal graph visualisation seeks to address the challenges involved in visually representing change in a graph over time. While most graph visualisation tools focus on static networks, recent research has been directed toward the development of temporal visualisation systems. By representing data using computer-generated graphical forms, Information Visualisation techniques harness human perceptual capabilities to recognise patterns, spot anomalies and outliers, and find relationships within the data. Interacting with these graphical representations allow individuals to explore large datasets and gain further insightinto the relationships between different aspects of the data. Visual approaches are particularly relevant for Exploratory Data Analysis (EDA), where the person performing the analysis may be unfamiliar with the data set, and their goal is to make new discoveries and gain insight through its exploration. However, designing visual systems for EDA can be difficult, as the tasks which a person may wish to carry out during their analysis are not always known at outset. Identifying and understanding the tasks involved in such a process has given rise to a number of task taxonomies which seek to elucidate the tasks and structure them in a useful way. While task taxonomies for static graph analysis exist, no suitable temporal graph taxonomy has yet been developed. The first part of this thesis focusses on the development of such a taxonomy. Through the extension and instantiation of an existing formal task framework for general EDA, a task taxonomy and a task design space are developed specifically for exploration of temporal graph data. The resultant task framework is evaluated with respect to extant classifications and is shown to address a number of deficiencies in task coverage in existing works. Its usefulness in both the design and evaluation processes is also demonstrated. Much research currently surrounds the development of systems and techniques for visual exploration of temporal graphs, but little is known about how the different types of techniques relate to one another and which tasks they are able to support. The second part of this thesis focusses on the possibilities in this area: a design spaceof the possible visual encodings for temporal graph data is developed, and extant techniques are classified into this space, revealing potential combinations of encodings which have not yet been employed. These may prove interesting opportunities for further research and the development of novel techniques. The third part of this work addresses the need to understand the types of analysis the different visual techniques support, and indeed whether new techniques are required. The techniques which are able to support the different task dimensions are considered. This task-technique mapping reveals that visual exploration of temporalgraph data requires techniques not only from temporal graph visualisation, but also from static graph visualisation and comparison, and temporal visualisation. A number of tasks which are unsupported or less-well supported, which could prove interesting opportunities for future research, are identified. The taxonomies, design spaces, and mappings in this work bring order to the range of potential tasks of interest when exploring temporal graph data and the assortmentof techniques developed to visualise this type of data, and are designed to be of use in both the design and evaluation of temporal graph visualisation systems.

Manual material handling (MMH) tasks are associated with the development of work-related musculoskeletal disorders (WMSDs). Minimizing the frequency and intensity of handling objects is an ideal solution, yet MMH remains an integral part of many industry sectors, including manufacturing, construction, warehousing, and distribution. Physical exposure assessment can help identify high-risk tasks, guide the development and evaluation of ergonomic interventions, and contribute to understanding exposure-risk relationships. Physical exposure can be evaluated using self-assessment, observational methods, and direct measurements. Nevertheless, implementing these methods in situ can be challenging, time consuming, expensive, and infeasible or inaccurate in many cases. Thus, there is a critical need to improve physical exposure assessments to protect workers and save costs. This dissertation assessed the accuracy of a markerless motion capture system (MMC) to quantify physical exposures during MMH tasks using three studies. Specifically, the first study investigated the performance of an MMC system, together with machine learning algorithms, for classifying diverse MMH tasks during a simulated complex job. In the second study, the feasibility of predicting dynamic hand forces was determined, using alternative measures, such as kinematics from MMC and/or in-sole pressure systems, coupled with a machine learning algorithm. Finally, in the third study, we systematically evaluated MMC for assessing biomechanical demands, by comparing outputs from a full-body musculoskeletal model driven by kinematic and kinetics from gold standard input and estimates derived from the MMC and in-sole pressure measurement system. Overall, the findings of these studies demonstrated the potential of using MMC to classify several common occupational tasks and to estimate the associated biomechanical demands for a given worker (automatically and with minimal physical contact). Additionally, the methods developed here can help stakeholders rapidly assess an individual worker's exposure to physical demands during diverse tasks. / Doctor of Philosophy / Manual material handling (MMH) tasks expose workers to known risk factors for work-related musculoskeletal disorders (WMSDs) such as back and shoulder pain. Accurately quantifying workplace exposures to these risk factors is an essential aspect of identifying high-risk working conditions and for developing/evaluating workplace interventions to reduce WMSD risks. Current physical exposure assessment tools are labor-intensive, offer crude measures, and have limited application due to costs or feasibility. Using markerless motion capture (MMC) systems in the workplace could enable full or partial automation for the collection of critical measures such as the tasks a worker performs, the hand forces involved, and their biomechanical demands. New approaches are needed, though, since such automation is challenging due to variations in the type of input data required for different physical exposure assessments. In this dissertation, our goal was to assess the accuracy of MMC as a tool to quantify physical exposures during MMH tasks. In support of our goal, three studies were completed. In the first study, we investigated the accuracy of using data from MMC together with machine learning algorithms to classify diverse MMH tasks, and distinguish among different task conditions. Our results emphasized that classification performance was satisfactory, though it differed between feature sets, MMH tasks, and between males and females. The second study explored combining MMC and IPM data with machine learning algorithms to predict hand forces during MMH tasks. Our results were encouraging overall, but predictions were less accurate in pushing and pulling tasks. In the third study, we evaluated an approach for estimating biomechanical demands on data obtained from MMC and in-sole pressure measurement systems. We compared estimates from a musculoskeletal model driven by kinematics from a whole-body inertial measurement unit and kinetics from direct measures of hand loads, and kinematics from MMC. Our findings support using MMC and kinetics from predicted hand forces as input for estimating biomechanical demands. Overall, findings from these studies show that MMC can automatically classify common occupational tasks, predict dynamic hand forces, and estimate biomechanical demands with minimal physical contact. This new approach could allow stakeholders to assess worker's exposure and the efficiency of ergonomic interventions.