Computing and Information Systems - Theses
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ItemReal-time feedback for surgical simulation using data mining( 2014)Surgical trainees devote years to master the surgical skills required to safely perform surgery. Traditionally, they refine their psychomotor skills by practising on plastic bones or cadavers under the supervision of expert surgeons. Experts guide trainees through surgical procedures while providing feedback on the quality of their procedure. However, there are limitations to this approach, which include a shortage of cadaver bones, limited availability of expert supervision, and the subjective manner of surgical skill assessment. To address these limitations, the introduction of new techniques such as 3D illusion, haptic feedback and augmented reality have significantly improved the realism of surgical simulators. Such simulators have the potential to provide a cost-effective platform, which allows trainees to practice many surgical cases of varying difficulty, and provides the flexibility of practising repeatedly at their own convenience. However, most simulators lack the automated performance assessment and feedback, which limits the applicability of simulators as self-guided training systems. In thesis, we aim to deliver automated performance assessment and feedback in a virtual simulation environment. The automated performance assessment provides information on the quality of surgical result, which is a critical component for a self-guided training platform. A large number of recent studies have focused on scoring the outcome of surgical tasks. However, this score typically based on the result of a surgical task (such as the shape of a surgical end-product) and ignores the rich information provided by real-time performance attributes, such as motion records. Furthermore, since this assessment is delivered at the end of each task, it does not allow any opportunity to identify and address mistakes as they occur. We propose an event-based framework that provides online assessment with different temporal granularities. The evaluations show that the proposed framework provides accurate performance assessment using both motion records and end-product information. Although automated performance assessment provides expertise score to illustrate the surgical perform, a single score has limited utility in improving surgical technique, which can be equally important. Trainees need constructive human understandable feedback to refine their psychomotor skills. To this end, we propose a Random Forest based approach to generate meaningful automated real-time performance feedback. Our evaluation demonstrates it can significantly improve the surgical techniques. However, this random forest based method makes specific assumptions that all drilling movements made by experts are of "expert quality'' and all operations made by trainees are suboptimal. This hampers the model training process and leads to lower accuracy rates. Therefore we propose a pattern-based approach to capture the differences in technique between experts and trainees and deliver real-time feedback to improve performance, while avoiding the assumption of "polarising'' the quality of drill strokes based on expertise. Our evaluation results show that the proposed approach identifies the stage of the surgical procedure correctly and provides constructive feedback to assist surgical trainees in improving their technique. Another challenge for automated performance assessment is hard to extend existing evaluation models to new specimens. In order to train reliable assessment models for new specimens, the classical machine learning approaches require a new set of human expert examples collected from each new specimen. To eliminate this need, we propose a transfer learning framework to adapt a classifier built on a single specimen to multiple specimens. Once a classifier is trained, we translate the new specimens' features to the original feature space, which allows us to carry out performance evaluation on different specimens using the same classifier. In summary, the major contributions of this thesis involve the development of self-guided training platform for delivering automatic assessment and feedback using data mining techniques.
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ItemDistribution enhanced data mining methods for time series forecasting( 2014)Time series forecasting is an exciting research area whose challenges are to discover patterns from data that has been observed over time. Whether it is stock market variables concerning price behaviour, or weather measurements that can be used to issue a timely warning of an approaching cyclone, or radiation level measurements that can prevent a harmful disaster and save hundreds of lives in a power plant, time series find useful applications in many diverse sciences and disciplines. The behaviour of time series is susceptible to changes, often reflected through distribution features such as mean and variance. Though changes in the series are to be expected, they are of mostly a continuous nature, meaning that predictions to a certain point in the future can be made with a high degree of accuracy. Understanding how these changes affect the prediction accuracy is crucial to minimizing the forecasted error. This thesis investigates utilising information about changes in the series, and presents novel modifications in the learning process based on the knowledge gained about these changes. There are four main contributions presented in this thesis, which deliver innovative techniques for time series analysis by incorporating distribution characteristics. In the first part of the thesis we develop a pre-processing algorithm that uses distribution information which can easily accompany any prediction model we choose to work with. Our algorithm performs a fast and efficient reduction of the training samples depending on the change of the mean of the distribution, leaving a set of samples with a larger concentration of useful information and reduced noise. In the next part of our work, we introduce an intelligent group-based error minimization algorithm, which simultaneously achieves reduction of both mean and variance of the forecasted errors, associated with groups of observations with similar distribution. We demonstrate how this sensitive grouping of samples reduces both the error and variance of the error per group, embodied in a modified linear regression algorithm. We then introduce a modified form of Support Vector Regression that detects potential large-error producing samples, and which penalizes the loss for these samples by using a time-sensitive loss, for directly targeting a reduction of the variance in the forecasted errors. This new approach achieves competitive reduction in the error variance and produces more accurate predictions, with performance better than several state of the art methods. Finally, we apply our technical methodologies for the purpose of discrimination aware learning, where we demonstrate how they can be modified and used in another research context.