Effective skill refinement: Focusing on process to ensure outcome

In contrast to the abundance of motor skill acquisition and performance research, there is a paucity of work which addresses how athletes with an already learnt and well-established skill may go about making a subtle change, or refinement, to that skill. Accordingly, the purpose of this review paper is to provide a comprehensive overview of current understanding pertaining to such practice. Specifically, this review addresses deliberately initiated refinements to closed and self-paced skills (e.g., javelin throwing, golf swing and horizontal jumps). In doing so, focus is directed to three fundamental considerations within applied coaching practice and future research endeavours; the intended outcomes, process and evaluative measures of skill refinement. Conclusions suggest that skill refinement is not the same as skill acquisition or performing already learnt skills with high-levels of automaticity. Due to the complexity of challenge faced, refinements are best addressed as an interdisciplinary solution, with objective measures informing coach decision making

Extraction and Classification of Diving Clips from Continuous Video Footage

Due to recent advances in technology, the recording and analysis of video data has become an increasingly common component of athlete training programmes. Today it is incredibly easy and affordable to set up a fixed camera and record athletes in a wide range of sports, such as diving, gymnastics, golf, tennis, etc. However, the manual analysis of the obtained footage is a time-consuming task which involves isolating actions of interest and categorizing them using domain-specific knowledge. In order to automate this kind of task, three challenging sub-problems are often encountered: 1) temporally cropping events/actions of interest from continuous video; 2) tracking the object of interest; and 3) classifying the events/actions of interest. Most previous work has focused on solving just one of the above sub-problems in isolation. In contrast, this paper provides a complete solution to the overall action monitoring task in the context of a challenging real-world exemplar. Specifically, we address the problem of diving classification. This is a challenging problem since the person (diver) of interest typically occupies fewer than 1% of the pixels in each frame. The model is required to learn the temporal boundaries of a dive, even though other divers and bystanders may be in view. Finally, the model must be sensitive to subtle changes in body pose over a large number of frames to determine the classification code. We provide effective solutions to each of the sub-problems which combine to provide a highly functional solution to the task as a whole. The techniques proposed can be easily generalized to video footage recorded from other sports.Comment: To appear at CVsports 201

RGB-D-based Action Recognition Datasets: A Survey

Human action recognition from RGB-D (Red, Green, Blue and Depth) data has attracted increasing attention since the first work reported in 2010. Over this period, many benchmark datasets have been created to facilitate the development and evaluation of new algorithms. This raises the question of which dataset to select and how to use it in providing a fair and objective comparative evaluation against state-of-the-art methods. To address this issue, this paper provides a comprehensive review of the most commonly used action recognition related RGB-D video datasets, including 27 single-view datasets, 10 multi-view datasets, and 7 multi-person datasets. The detailed information and analysis of these datasets is a useful resource in guiding insightful selection of datasets for future research. In addition, the issues with current algorithm evaluation vis-\'{a}-vis limitations of the available datasets and evaluation protocols are also highlighted; resulting in a number of recommendations for collection of new datasets and use of evaluation protocols

Event detection in field sports video using audio-visual features and a support vector machine

In this paper, we propose a novel audio-visual feature-based framework for event detection in broadcast video of multiple different field sports. Features indicating significant events are selected and robust detectors built. These features are rooted in characteristics common to all genres of field sports. The evidence gathered by the feature detectors is combined by means of a support vector machine, which infers the occurrence of an event based on a model generated during a training phase. The system is tested generically across multiple genres of field sports including soccer, rugby, hockey, and Gaelic football and the results suggest that high event retrieval and content rejection statistics are achievable

The development and applications of serious games in the public services: defence and health

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.The latest advances of Virtual Reality technologies and three-dimensional graphics, as well as the developments in Gaming Technologies in the recent years, have stemmed the proliferation of Serious Games in a broader spectrum of research applications. Among the most popular areas of application are public services such as Defence and Health, where digital technologies realise new challenges and opportunities for research and development of Serious Games and for a variety of contexts. As with all games, the user engagement is elevated and apart from the entertaining aspect, Serious Games serve as a novel and promising alternative experience to knowledge transfer. Furthermore, Serious Games bring to the end user and the overall society a series of attractive benefits. These benefits include safety, cost-effectiveness, increased motivation and personalisation. Hence, this Thesis aims to investigate novel approaches of developing Serious Games that utilise the recent advances of Virtual Reality and Gaming Technology and facilitate the aforementioned benefits. The process of design and development of the novel tools and applications follow an iterative manner and are driven by the review of the available literature as well as end-user feedbackEPSRC (Engineering and Physical Sciences Research Council ) , MOD (UK), NHS (UK

The role of biomechanics in achieving different shot trajectories in golf

In golf, a range of shot types are necessary for successful performance, with driving and iron-play constituting the long-game. It is possible to vary long-game shots through altered trajectory, for example, by utilising right-to-left or left-to-right ball flight curvature, providing course management advantages. However, how golfers vary their biomechanics to achieve different trajectories is not scientifically understood. Therefore, the purpose of this thesis was to biomechanically investigate different trajectories hit with the same club. To investigate shot trajectories, accurate measures of performance were necessary. Launch monitors (TrackMan Pro IIIe and Foresight GC2+HMT) are bespoke technologies capable of tracking the clubhead and ball through impact. However, their accuracy for scientific research has not been independently validated. Therefore, a novel purpose-designed tracking method was developed using a three-dimensional optical tracking system (GOM). The accuracy of this method was validated and the system used as the benchmark to which the two launch monitors were compared through limits of agreement. The results showed, in general, the launch monitors were in closer agreement to the benchmark for ball parameters than clubhead. High levels of agreement were found for ball velocity, ball path, total spin rate and backspin. However, poorer agreement was shown for ball sidespin and spin axis as well as clubhead velocity, clubhead path and clubhead orientation. Consequently, the launch monitors were deemed unsuitable for inclusion in scientific research across a range of impact parameters. Draw and fade trajectories with a driver and draw, fade and low trajectories with a 5-iron were investigated biomechanically. The clubhead and ball were tracked using the optical method developed in this thesis. Key biomechanical variables (address position and whole-swing) were defined based on coaching theory. Statistically, analysis of variance (address) and principal components analysis (whole-swing), were used to compare draw against fade and low against natural trajectories. Multivariate correlation was used to identify swing pattern similarities between golfers. The group-level comparison showed draw-fade address differences whereby for draw trajectories, the ball was positioned further away from the target, the lead hand further towards the target and the pelvis, thorax and stance openness closed relative to the target line. Over the whole-swing, the draw when compared to the fade demonstrated a pelvis rotation, more rotated away from the target with later rotation; lumbar forward flexion, with slower extending in the downswing; lumbar lateral flexion, with more flexion towards the trail throughout and prolonged trail flexing through ball contact; thorax lateral flexion, with greater, slower lead flexing in the backswing and greater, more prolonged trail flexing in the downswing; pelvis translation further towards the target throughout, with earlier forward translation and centre of pressure, with an earlier, quicker, greater forward shift. Cluster differences were evident, with both Clusters I (57% of golfers with the driver) and II (71% of golfers with the 5-iron) showing greater, earlier thorax rotation towards the target and a tendency for greater lumbar forward flexion over the whole-swing (Cluster II) and backswing (Cluster I). For the group-level low-natural comparison, golfers positioned the ball further away from the target and their lead hand further towards the target for low trajectories. Further, Cluster IV (45% of golfers), narrowed their stance width and laterally flexed their thorax towards the lead, for the same trajectories. Over the whole-swing, the low when compared to the natural showed the pelvis translated towards the target throughout, with later, lesser forward shift for the low trajectories. Furthermore, centre of pressure displayed a greater forward shift for the same shots. Finally, both clusters (Cluster III 36% of golfers and Cluster IV) differed in lumbar forward flexion when playing low trajectories; over the backswing, Cluster III extended, whereas Cluster IV flexed. Cluster IV also showed greater extending in the downswing. Finally, Cluster IV showed more lumbar lateral flexion towards the lead throughout. The results of this study have implications for scientific researchers as well as golf coaches, club-fitters and professionals. Commercially available launch monitors appear accurate enough for coaching applications, however caution is needed for scientific research when tracking a range of clubhead and ball parameters. Furthermore, changes in biomechanics when playing different trajectories has implications for future research and interpretation of published work, as well as for coaching theory. Future work following this thesis could utilise the optical tracking method to validate further commercial systems and for more detailed experimental investigation of clubhead-ball impacts. Furthermore, additional biomechanical investigation into a wider range of shot trajectories across more variables could be conducted, with a more in-depth understanding gained from principal components analysis and golfer clustering

Benchmarking Cerebellar Control

Cerebellar models have long been advocated as viable models for robot dynamics control. Building on an increasing insight in and knowledge of the biological cerebellum, many models have been greatly refined, of which some computational models have emerged with useful properties with respect to robot dynamics control. Looking at the application side, however, there is a totally different picture. Not only is there not one robot on the market which uses anything remotely connected with cerebellar control, but even in research labs most testbeds for cerebellar models are restricted to toy problems. Such applications hardly ever exceed the complexity of a 2 DoF simulated robot arm; a task which is hardly representative for the field of robotics, or relates to realistic applications. In order to bring the amalgamation of the two fields forwards, we advocate the use of a set of robotics benchmarks, on which existing and new computational cerebellar models can be comparatively tested. It is clear that the traditional approach to solve robotics dynamics loses ground with the advancing complexity of robotic structures; there is a desire for adaptive methods which can compete as traditional control methods do for traditional robots. In this paper we try to lay down the successes and problems in the fields of cerebellar modelling as well as robot dynamics control. By analyzing the common ground, a set of benchmarks is suggested which may serve as typical robot applications for cerebellar models