Precision biomechanical motion tracking and throw characterisation in professional darts

In professional sports there are in general three steps required to improve performance namely task definition, training and performance assessment. This process is iteratively repeated and feedback generated from quantitative performance measurement is in turn used for task redefinition. Task definition can be achieved in a number of ways including via video streaming or indeed and as is more common, by listening to coaching staff. However non-subjective performance evaluation is difficult due to the complexity of the movements involved. When considering the subset of sports where precision accuracy and repeatability are a necessity this problem becomes inherently more difficult to solve. Until recently sports such as martial arts, fencing and darts, where the smallest deviation from a prescribed movement goal can result in large outcome error, were deemed too difficult to characterise fully. Advances in technology, as illustrated by this study, now make this type of physiometry possible.

An automated calibration tool for high performance wireless inertial measurement in professional sports

Traditional motion capture techniques, for instance, those employing optical technology, have long been used in the area of rehabilitation, sports medicine and performance analysis, where accurately capturing bio-mechanical data is of crucial importance. However their size, cost, complexity and lack of portability mean that their use is often impractical. Low cost MEMS inertial sensors when combined and assembled into a Wireless Inertial Measurement Unit (WIMU) present a possible solution for low cost and highly portable motion capture. However due to the large variability inherent to MEMS sensors, such a system would need extensive characterization to calibrate each sensor and ensure good quality data capture. A completely calibrated WIMU system would allow for motion capture in a wider range of real-world, non-laboratory based applications. Calibration can be a complex task, particularly for newer, multi-sensing range capable inertial sensors. As such we present an automated system for quickly and easily calibrating inertial sensors in a packaged WIMU, demonstrating some of the improvements in accuracy attainable.

Finding optimal alternatives based on efficient comparative preference inference

Choosing the right or the best option is often a demanding and challenging task for the user (e.g., a customer in an online retailer) when there are many available alternatives. In fact, the user rarely knows which offering will provide the highest value. To reduce the complexity of the choice process, automated recommender systems generate personalized recommendations. These recommendations take into account the preferences collected from the user in an explicit (e.g., letting users express their opinion about items) or implicit (e.g., studying some behavioral features) way. Such systems are widespread; research indicates that they increase the customers' satisfaction and lead to higher sales. Preference handling is one of the core issues in the design of every recommender system. This kind of system often aims at guiding users in a personalized way to interesting or useful options in a large space of possible options. Therefore, it is important for them to catch and model the user's preferences as accurately as possible. In this thesis, we develop a comparative preference-based user model to represent the user's preferences in conversational recommender systems. This type of user model allows the recommender system to capture several preference nuances from the user's feedback. We show that, when applied to conversational recommender systems, the comparative preference-based model is able to guide the user towards the best option while the system is interacting with her. We empirically test and validate the suitability and the practical computational aspects of the comparative preference-based user model and the related preference relations by comparing them to a sum of weights-based user model and the related preference relations. Product configuration, scheduling a meeting and the construction of autonomous agents are among several artificial intelligence tasks that involve a process of constrained optimization, that is, optimization of behavior or options subject to given constraints with regards to a set of preferences. When solving a constrained optimization problem, pruning techniques, such as the branch and bound technique, point at directing the search towards the best assignments, thus allowing the bounding functions to prune more branches in the search tree. Several constrained optimization problems may exhibit dominance relations. These dominance relations can be particularly useful in constrained optimization problems as they can instigate new ways (rules) of pruning non optimal solutions. Such pruning methods can achieve dramatic reductions in the search space while looking for optimal solutions. A number of constrained optimization problems can model the user's preferences using the comparative preferences. In this thesis, we develop a set of pruning rules used in the branch and bound technique to efficiently solve this kind of optimization problem. More specifically, we show how to generate newly defined pruning rules from a dominance algorithm that refers to a set of comparative preferences. These rules include pruning approaches (and combinations of them) which can drastically prune the search space. They mainly reduce the number of (expensive) pairwise comparisons performed during the search while guiding constrained optimization algorithms to find optimal solutions. Our experimental results show that the pruning rules that we have developed and their different combinations have varying impact on the performance of the branch and bound technique.

Study abroad and complexity, accuracy and fluency (CAF) development: a longitudinal investigation of French and Chinese Learners of L2 English

This longitudinal study tracked third-level French (n=10) and Chinese (n=7) learners of English as a second language (L2) during an eight-month study abroad (SA) period at an Irish university. The investigation sought to determine whether there was a significant relationship between length of stay (LoS) abroad and gains in the learners' oral complexity, accuracy and fluency (CAF), what the relationship was between these three language constructs and whether the two learner groups would experience similar paths to development. Additionally, the study also investigated whether specific reported out-of-class contact with the L2 was implicated in oral CAF gains. Oral data were collected at three equidistant time points; at the beginning of SA (T1), midway through the SA sojourn (T2) and at the end (T3), allowing for a comparison of CAF gains arising during one semester abroad to those arising during a subsequent semester. Data were collected using Sociolinguistic Interviews (Labov, 1984) and adapted versions of the Language Contact Profile (Freed et al., 2004). Overall, the results point to LoS abroad as a highly influential variable in gains to be expected in oral CAF during SA. While one semester in the TL country was not enough to foster statistically significant improvement in any of the CAF measures employed, significant improvement was found during the second semester of SA. Significant differences were also revealed between the two learner groups. Finally, significant correlations, some positive, some negative, were found between gains in CAF and specific usage of the L2. All in all, the disaggregation of the group data clearly illustrates, in line with other recent enquiries (e.g. Wright and Cong, 2014) that each individual learner's path to CAF development was unique and highly individualised, thus providing strong evidence for the recent claim that SLA is "an individualized nonlinear endeavor" (Polat and Kim, 2014: 186).