7,386 research outputs found

    The role of motion analysis in elite soccer

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    The optimal physical preparation of elite soccer (association football) players has become an indispensable part of the professional game especially due to the increased physical demands of match-play. The monitoring of players’ work-rate profiles during competition is now feasible through computer-aided motion analysis. Traditional methods of motion analysis were extremely labour intensive and were largely restricted to university- based research projects. Recent technological developments have meant that sophisticated systems, capable of quickly recording and processing the data of all players’ physical contributions throughout an entire match, are now being used in elite club environments. In recognition of the important role motion analysis now plays as a tool for measuring the physical performance of soccer players, this review critically appraises various motion analysis methods currently employed in elite soccer and explores research conducted using these methods. This review therefore aims to increase the awareness of both practitioners and researchers of the various motion analysis systems available, identify practical implications of the established body of knowledge, while highlighting areas that require further exploration

    VCoach: A Customizable Visualization and Analysis System for Video-based Running Coaching

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    Videos are accessible media for analyzing sports postures and providing feedback to athletes. Existing video-based coaching systems often present feedback on the correctness of poses by augmenting videos with visual markers either manually by a coach or automatically by computing key parameters from poses. However, previewing and augmenting videos limit the analysis and visualization of human poses due to the fixed viewpoints, which confine the observation of captured human movements and cause ambiguity in the augmented feedback. Besides, existing sport-specific systems with embedded bespoke pose attributes can hardly generalize to new attributes; directly overlaying two poses might not clearly visualize the key differences that viewers would like to pursue. To address these issues, we analyze and visualize human pose data with customizable viewpoints and attributes in the context of common biomechanics of running poses, such as joint angles and step distances. Based on existing literature and a formative study, we have designed and implemented a system, VCoach, to provide feedback on running poses for amateurs. VCoach provides automatic low-level comparisons of the running poses between a novice and an expert, and visualizes the pose differences as part-based 3D animations on a human model. Meanwhile, it retains the users' controllability and customizability in high-level functionalities, such as navigating the viewpoint for previewing feedback and defining their own pose attributes through our interface. We conduct a user study to verify our design components and conduct expert interviews to evaluate the usefulness of the system

    Biomechanical analysis and model development applied to table tennis forehand strokes

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    Table tennis playing involves complex spatial movement of the racket and human body. It takes much effort for the novice players to better mimic expert players. The evaluation of motion patterns during table tennis training, which is usually achieved by coaches, is important for novice trainees to improve faster. However, traditional coaching relies heavily on coaches qualitative observation and subjective evaluation. While past literature shows considerable potential in applying biomechanical analysis and classification for motion pattern assessment to improve novice table tennis players, little published work was found on table tennis biomechanics. To attempt to overcome the problems and fill the gaps, this research aims to quantify the movement of table tennis strokes, to identify the motion pattern differences between experts and novices, and to develop a model for automatic evaluation of the motion quality for an individual. Firstly, a novel method for comprehensive quantification and measurement of the kinematic motion of racket and human body is proposed. In addition, a novel method based on racket centre velocity profile is proposed to segment and normalize the motion data. Secondly, a controlled experiment was conducted to collect motion data of expert and novice players during forehand strokes. Statistical analysis was performed to determine the motion differences between the expert and the novice groups. The experts exhibited significantly different motion patterns with faster racket centre velocity and smaller racket plane angle, different standing posture and joint angular velocity, etc. Lastly, a support vector machine (SVM) classification technique was employed to build a model for motion pattern evaluation. The model development was based on experimental data with different feature selection methods and SVM kernels to achieve the best performance (F1 score) through cross-validated and Nelder-Mead method. Results showed that the SVM classification model exhibited good performance with an average model performance above 90% in distinguishing the stroke motion between expert and novice players. This research helps to better understand the biomechanical mechanisms of table tennis strokes, which will ultimately aid the improvement of novice players. The phase segmentation and normalization methods for table tennis strokes are novel, unambiguous and straightforward to apply. The quantitative comparison identified the comprehensive differences in motion between experts and novice players for racket and human body in continuous phase time, which is a novel contribution. The proposed classification model shows potential in the application of SVM to table tennis biomechanics and can be exploited for automatic coaching

    Inside the brain of an elite athlete: The neural processes that support high achievement in sports

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    Events like the World Championships in athletics and the Olympic Games raise the public profile of competitive sports. They may also leave us wondering what sets the competitors in these events apart from those of us who simply watch. Here we attempt to link neural and cognitive processes that have been found to be important for elite performance with computational and physiological theories inspired by much simpler laboratory tasks. In this way we hope to inspire neuroscientists to consider how their basic research might help to explain sporting skill at the highest levels of performance

    Multi-sensor human action recognition with particular application to tennis event-based indexing

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    The ability to automatically classify human actions and activities using vi- sual sensors or by analysing body worn sensor data has been an active re- search area for many years. Only recently with advancements in both fields and the ubiquitous nature of low cost sensors in our everyday lives has auto- matic human action recognition become a reality. While traditional sports coaching systems rely on manual indexing of events from a single modality, such as visual or inertial sensors, this thesis investigates the possibility of cap- turing and automatically indexing events from multimodal sensor streams. In this work, we detail a novel approach to infer human actions by fusing multimodal sensors to improve recognition accuracy. State of the art visual action recognition approaches are also investigated. Firstly we apply these action recognition detectors to basic human actions in a non-sporting con- text. We then perform action recognition to infer tennis events in a tennis court instrumented with cameras and inertial sensing infrastructure. The system proposed in this thesis can use either visual or inertial sensors to au- tomatically recognise the main tennis events during play. A complete event retrieval system is also presented to allow coaches to build advanced queries, which existing sports coaching solutions cannot facilitate, without an inordi- nate amount of manual indexing. The event retrieval interface is evaluated against a leading commercial sports coaching tool in terms of both usability and efficiency

    Pedagogical Feedback for Computer-based Sport Training

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    Feedback in Computer-based Sport Training (CBST) may be synthetically designed to allow athletes to practise in a more effective way and enhance their skill acquisition. Little research has integrated pedagogic theory and instructional design with the design of feedback in CBST. To bridge this gap, the paper presents the design of pedagogically-informed feedback for the implementation of a CBST system. The heart of the design is to generate feedback based on the athletes’ achievement of their intended training outcome. The pedagogical feedback system measures athletes’ performance and compares it with the given training outcomes. The system then identifies the performance’s gap and generates feedback to reinforce better performance. A Counterbalanced experiment asked student rowers (N = 8) to explore the differences between the pedagogical feedback system and their current feedback system (Sean-Analysis). Pedagogical feedback was at least as good as Sean-Analysis with respect to the level of satisfaction of the athlete. Overall, it can be concluded that the pedagogical feedback appears to be a good model for generating feedback in CBST

    VIRD: Immersive Match Video Analysis for High-Performance Badminton Coaching

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    Badminton is a fast-paced sport that requires a strategic combination of spatial, temporal, and technical tactics. To gain a competitive edge at high-level competitions, badminton professionals frequently analyze match videos to gain insights and develop game strategies. However, the current process for analyzing matches is time-consuming and relies heavily on manual note-taking, due to the lack of automatic data collection and appropriate visualization tools. As a result, there is a gap in effectively analyzing matches and communicating insights among badminton coaches and players. This work proposes an end-to-end immersive match analysis pipeline designed in close collaboration with badminton professionals, including Olympic and national coaches and players. We present VIRD, a VR Bird (i.e., shuttle) immersive analysis tool, that supports interactive badminton game analysis in an immersive environment based on 3D reconstructed game views of the match video. We propose a top-down analytic workflow that allows users to seamlessly move from a high-level match overview to a detailed game view of individual rallies and shots, using situated 3D visualizations and video. We collect 3D spatial and dynamic shot data and player poses with computer vision models and visualize them in VR. Through immersive visualizations, coaches can interactively analyze situated spatial data (player positions, poses, and shot trajectories) with flexible viewpoints while navigating between shots and rallies effectively with embodied interaction. We evaluated the usefulness of VIRD with Olympic and national-level coaches and players in real matches. Results show that immersive analytics supports effective badminton match analysis with reduced context-switching costs and enhances spatial understanding with a high sense of presence.Comment: To Appear in IEEE Transactions on Visualization and Computer Graphics (IEEE VIS), 202
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